Hi I am Rajesh. As a business consultant my clients are always asking about implementing of AI and Machine Learning in there business. And what are the factors that effect on business.

In recent years, artificial intelligence (AI) and machine learning (ML) have shifted from futuristic concepts to everyday technologies that are driving change in various industries. For startups, these tools can be especially powerful in enabling growth, streamlining operations, and creating new value for customers. Whether you're a tech-driven company or not, leveraging AI and ML can position your startup to compete with established players and scale faster. Let's dive into why and how startups can leverage AI and ML to transform their businesses.

Understanding the Basics of AI and ML#

First, it's important to distinguish between AI and ML. AI is a broader concept where machines simulate human intelligence, while ML is a subset of AI focused on enabling machines to learn from data. By analyzing patterns in data, ML allows systems to make decisions, improve over time, and even predict future outcomes without being explicitly programmed for each task.For startups, ML can unlock a range of capabilities: predictive analytics, personalization, and automation, to name a few. These capabilities often translate into increased efficiency, improved customer experience, and new data-driven insights. Artificial intelligence (AI) and machine learning (ML) offer startups powerful tools to accelerate growth, streamline operations, and gain competitive advantages. Here's a breakdown of how these technologies can help startups across various aspects of their business:

Enhanced Customer Experience#

• Personalization: ML algorithms analyze customer data to understand individual preferences and behaviors. This allows startups to provide personalized product recommendations, content suggestions, or offers that resonate with each user, boosting engagement and satisfaction.

• Customer Support: AI-powered chatbots and virtual assistants can handle customer inquiries, provide instant support, and resolve common issues, reducing response times and freeing up human agents for more complex queries. This helps in maintaining high-quality customer service even with limited resources.

Data-Driven Decision Making#

• Predictive Analytics: Startups can leverage ML to analyze historical data and identify trends, enabling them to forecast demand, customer behavior, and potential risks. This helps in making strategic decisions based on data-driven insights rather than intuition.

-Automated Insights: With AI, startups can automate data analysis, turning raw data into actionable insights. This allows decision-makers to quickly understand business performance and make informed adjustments in real time.

Operational Efficiency#

• Process Automation: Startups can automate routine and repetitive tasks using AI, such as data entry, scheduling, and reporting. This not only saves time and reduces errors but also allows teams to focus on higher-value tasks that drive growth.

• Resource Optimization: ML can help optimize resources like inventory, workforce, and capital by analyzing usage patterns. For example, an e-commerce startup could use AI to manage inventory levels based on predicted demand, minimizing waste and avoiding stockouts.

Improved Marketing and Sales#

• Targeted Marketing Campaigns: AI enables startups to segment audiences more precisely, allowing for targeted campaigns tailored to specific customer groups. This leads to higher conversion rates and more effective marketing spend.

• Sales Forecasting: ML can analyze past sales data to predict future sales trends, helping startups set realistic targets and make strategic plans. This can also aid in understanding seasonality and customer buying cycles.

Fraud Detection and Security#

• Fraud Detection: For startups dealing with sensitive data or transactions, AI can identify unusual activity patterns that might indicate fraud. ML algorithms can analyze vast amounts of transaction data in real-time, flagging potential fraud and helping prevent financial loss.

• Enhanced Security: AI can bolster cybersecurity by continuously monitoring and identifying suspicious behavior, securing customer data, and reducing the likelihood of data breaches.

Product Development and Innovation#

• Rapid Prototyping: ML models can simulate different versions of a product, helping startups test ideas quickly and refine them based on data. This accelerates product development and reduces the risk of investing in features that don't resonate with users.

• New Product Features: AI can suggest new features based on user feedback and behavioral data. For example, a software startup might use AI to analyze user activity and identify popular or underused features, allowing for continuous improvement and customer-centric innovation.

Cost Reduction#

• Reduced Operational Costs: By automating repetitive tasks and optimizing resource allocation, AI helps startups cut down on overhead costs. For instance, a logistics startup could use ML to optimize delivery routes, saving fuel and labor costs.

• Lower Staffing Needs: AI-powered tools can handle various functions (e.g., customer support, data analysis), enabling startups to operate efficiently with lean teams, which is often essential when funds are limited.

Better Talent Management#

• Talent Sourcing: AI can help startups find and screen candidates by analyzing resumes, skills, and previous job performance, making the recruitment process faster and more efficient.

• Employee Engagement: ML can identify patterns that lead to high employee satisfaction, such as workload balance or career development opportunities. This enables startups to foster a positive work environment, reducing turnover and improving productivity.

Scalability and Flexibility#

• Scalable Solutions: AI tools are inherently scalable, meaning that as your business grows, you can adjust algorithms and data processing capabilities to match increased demand without substantial infrastructure investment.

• Adaptable Models: ML models can adapt over time as new data becomes available, making them more effective as your startup scales. This flexibility helps startups to maintain a competitive edge by continually improving predictions and automations.

Conclusion#

AI and ML provide startups with immense potential for innovation, allowing them to operate with agility, streamline operations, and provide highly personalized experiences for their customers. By carefully implementing these technologies, startups can optimize resources, drive sustainable growth, and remain competitive in an increasingly tech-driven market. Embracing AI and ML early can be a game-changing move, positioning startups for long-term success.

Hey there, fellow coders! 👋 Ever felt like you needed an extra pair of hands (or brains) while working on your projects? Well, guess what? The future is here, and it's brought a new side car buddy for us developers: Artificial Intelligence!

Don't worry if some of these terms are new to you - we'll break it all down in simple, easy-to-understand language. So grab your favourite chilled beverage ( I will grab my lotus biscoff 🤓🥂) , and let's explore this brave new world together!

What's DevOps? What's AI? And Why Should I Care?#

First things first, let's break down some terms:

• DevOps: Imagine if the people who write code (developers) and the people who manage where that code runs (operations) decided to be best friends and work super closely together. That's DevOps! It's all about teamwork making the dream work.

• AI (Artificial Intelligence): This is like teaching computers to think and learn. It's not quite like the robots in movies (yet), but it's still pretty cool!

• Generative AI: This is a special kind of AI and subset of ML that can create new stuff, like writing text or even code. Think of it as a super-smart assistant that can help you with your work.

Now, why should you care? Well, imagine having a tireless helper/expert/all rounder that can make your coding life easier and your projects run smoother. Sounds good, right? That's what happens when DevOps meets AI!

How AI is Accelerating DevOps world#

1. Writing / Assisting Code: Your New Pair Programming BuD#

Remember when you first learned to code, and you wished someone could sit next to you and construct your code ? Well, AI is here to be that "someone"! 👽🥂

Example: You're stuck on { "how to write a function, what does this line do, which library has this function" }. You type a comment: "// hey can you fix this function to validate the anomalies in a bunch of logs". Your AI buddy jumps in and suggests:

It's like having a super-smart friend looking over your shoulder, ready to help!

2. Testing Your Code: Finding Bugs Before that happens#

We all know testing is important, but let's be honest, it's not always the most exciting part of coding. LoL for me I always choose to hand it over to others. AI is here to make it easier and dare we say... fun?

Example: You've written a new feature for your app. Your AI testing tool might say: "I've run 100 tests on your new code. Good news: it works! Bad news: it only works on Sundays as the code was improperly written. Shall we fix that?"

3. Keeping Your Docs Fresh: Because "Check the Docs" Shouldn't Mean "Check the Dust". Well it would always be the case when I decided to doc 👨‍💻🤨.#

We all know we should keep our documentation updated. But who has the time? AI does!

Example: You make a small change to your code. Your AI doc helper pops up: "I've updated the README."

4. Helping Teams Work Together: The Universal Translator#

Ever felt like developers and managers speak different languages? AI is here to be your translator!

Example: In a meeting, a manager asks, "Hey there R@vi, Can we quickly build a sophisticated Devops BOT 🤖to simply our routine tasks." Your AI assistant powered by the Generative AI gets ready to fill your ✍️ editor.📝

5. Clarifying Misconceptions: AI is More Than Just a Single Tool#

It's well understood that DevOps is not just a single tool for managing workflows; it requires an integrated toolset to run efficiently. Similarly, AI isn't a one-button solution. By learning AI, you can harness its capabilities to optimize processes and simplify repetitive tasks.

But Wait, There's More (Challenges)!#

Of course, it's not all smooth driving. Here are a few things to keep in mind and be more attentive either:

1. Privacy Matters: Teaching AI with your code is great, but make sure it's not sharing your secrets! ( build your own model and self hosted / pick the commercial ones which adheres to all your compliance)

2. Don't let your learning's be away: AI is a helper, not a replacement. Keep learning and growing your own skills! ( feel like you are teaching your assistant to do your last tasks don't over reliance )

3. Double-Check the suggestions: AI is smart, but it's not perfect. Always review what it suggests.

Wrapping Up: The Future is Bright (and Probably Runs on AI)#

So there you have it! DevOps and AI are pairing up to make our lives as developers easier, more efficient, and maybe even a bit more fun 🤩 .

Remember, in this new world of AI-assisted DevOps, you're not just a coder - you're a tech wizard with a very clever wand. Use it wisely, and happy coding! 🚀👩‍💻👨‍💻

About Author#

Author Name:- Ravindra Sai Konna.#

Biography:-#

Ravindra Sai Konna is a seasoned AI & Security Researcher with a focus on AWS DevSecOps and AIoT (Artificial Intelligence of Things). With over half a decade of experience in the tech industry,

Passionate about knowledge sharing, Ravindra dedicates himself to extending research insights and guiding aspiring technologists. He plays a pivotal role in helping tech enthusiasts navigate and adopt new technologies.

Connect with Ravindra :#

LinkedIn: https://www.linkedin.com/in/ravindrasaikonna

Email: [email protected]

Computer vision is a branch of AI(Artificial Intelligence) that deals with visual data. The role of computer vision technology is to improve the way images and videos are interpreted by machines. It uses mathematics and analysis to extract important information from visual data.

There have been several innovations in computer vision technology in recent years. These innovations have significantly improved the speed and accuracy of AI.

Computer vision is a very important part of AI. Computer vision has many important AI applications like self-driving, facial recognition, medical imaging, etc. It also has many applications in different fields including security, entertainment, surveillance, and healthcare. Computer vision is enabling machines to become more intelligent with visual data.

All these innovations in computer vision make AI more human-like.

Computer Vision Technology#

In this article, we'll discuss recent innovations in computer vision technology that have improved AI. We will discuss advancements like object detection, pose estimation, semantic segmentation, and video analysis. We will also explore some applications and limitations of computer vision.

Image Recognition:#

Image recognition is a very important task in computer vision. It has many practical applications including object detection, facial recognition, image segmentation, etc.

In recent years there have been many innovations in image recognition that have led to improved AI. All the advancements in image recognition we see today have been possible due to deep learning, CNNs, Transfer technology, and GANs. We will explore each of these in detail.

Deep Learning#

Deep Learning is a branch of machine learning that has completely changed image recognition technology. It involves training models with a vast amount of complex data from images.

It uses mathematical models and algorithms to identify patterns from visual input. Deep learning has advanced image recognition technology so much that it can make informed decisions without a human host.

Convolutional Neural Networks (CNNs)#

Convolutional neural networks (CNNs) are another innovation in image recognition that has many useful applications. It consists of multiple layers that include a convolution layer, pool layer, and fully connected layer. The purpose of CNN is to identify, process, and classify visual data.

All of this is done through these three layers. The convolution layer identifies the input and extracts useful information. Then the pooling layer compresses it. Lastly, a fully connected layer classifies the information.

Transfer Learning#

Transfer learning means transferring knowledge of a pre-existing model. It is a technique used to save time and resources. In this technique instead of training an AI model with deep learning, a pre-existing model trained with vast amounts of data in the same field is used.

It gives many advantages like accurately saved costs and efficiency.

Generative Adversarial Network (GAN)#

GAN is another innovation in image recognition. It consists of two neural networks that are constantly in battle. One neural network produces data (images) and the other has to differentiate it as real or fake.

As the other network identifies images to be fake, the first network creates a more realistic image that is more difficult to identify. This cycle goes on and on improving results further.

Object Detection:#

Object detection is also a very crucial task in computer vision. It has many applications including self-driving vehicles, security, surveillance, and robotics. It involves detecting objects in visual data.

In recent years many innovations have been made in object detection. There are tons of object-detecting models. Each model offers a unique set of advantages.

Have a look at some of them.

Faster R-CNN#

Faster R-CNN (Region-based Convolutional Neural Network) is an object detection model that consists of two parts: Regional proposal network (RPN) and fast R-CNN. The role of RPN is to analyze data in images and videos to identify objects. It identifies the likelihood of an object being present in a certain area of the picture or video. It then sends a proposal to fast R-CNN which then provides the final result.

YOLO#

YOLO (You Only Look Once) is another popular and innovative object detection model. It has taken object detection to the next level by providing real-time accurate results. It is being used in Self-driving vehicles due to its speed. It uses a grid model for identifying objects. The whole area of the image/video is divided into grids. The AI model then analyzes each cell to predict objects.

Semantic Segmentation:#

Semantic segmentation is an important innovation in Computer vision. It is a technique in computer vision that involves labeling each pixel of an image/video to identify objects.

This technique is very useful in object detection and has many important applications. Some popular approaches to semantic segmentation are Fully Convolutional Networks (FCNs), U-Net, and Mask R-CNN.

Fully Convolutional Networks (FCNs)#

Fully convolutional networks (FCNs) are a popular approach used in semantic segmentation. They consist of a neural network that can make pixel-wise predictions in images and videos.

FCN takes input data and extracts different features and information from that data. Then that image is compressed and every pixel is classified. This technique is very useful in semantic segmentation and has applications in robotics and self-driving vehicles. One downside of this technique is that it requires a lot of training.

U-Net#

U-Net is another popular approach to semantic segmentation. It is popular in the medical field. In this architecture, two parts of U- Net one contracting and the other expanding are used for semantic segmentation.

Contracting is used to extract information from images/videos taken through U shaped tube. These images/videos are then processed to classify pixels and detect objects in that image/video. This technique is particularly useful for tissue imaging.

Mask R-CNN#

Mask R-CNN is another popular approach to semantic segmentation. It is a more useful version of Faster R-CNN which we discussed earlier in the Object detection section. It has all the features of faster R-CNN except it can segment the image and classify each pixel. It can detect objects in an image and segment them at the same time.

Pose Estimation:#

Pose estimation is another part of computer vision. It is useful for detecting objects and people in an image with great accuracy and speed. It has applications in AR (Augmented Reality), Movement Capture, and Robotics. In recent years there have been many innovations in pose estimation.

Here are some of the innovative approaches in pose estimation in recent years.

Open Pose#

The open pose is a popular approach to pose estimation. It uses CNN(Convolutional Neural Networks) to detect a human body. It identifies 135 features of the human body to detect movement. It can detect limbs and facial features, and can accurately track body movements.

Mask R-CNN#

Mask R-CNN can also be used for pose estimation. As we have discussed earlier object detection and semantic segmentation. it can extract features and predict objects in an object. It can also be used to segment different human body parts.

Video Analysis:#

Video Analysis is another important innovation in computer vision. It involves interpreting and processing data from videos. Video analysis consists of many techniques that include. Some of these techniques are video captioning, motion detection, and tracking.

Motion Detection#

Motion detection is an important task in video analysis. It involves detecting and tracking objects in a video. Motion detecting algorithm subtracts the background from a frame to identify an object then each frame is compared for tracking movements.

Video Captioning#

It involves generating natural text in a video. It is useful for hearing-impaired people. It has many applications in the entertainment and sports industry. It usually involves combining visuals from video and text from language models to give captions.

Tracking#

Tracking is a feature in video analysis that involves following the movement of a target object. Tracking has a wide range of applications in the sports and entertainment industry. The target object can be a human or any sports gear. For example, some common target objects are the tennis ball, hard ball, football, and baseball. Tracking is done by comparing consecutive frames for details.

Applications of Innovations in Computer Vision#

Innovations in computer vision have created a wide range of applications in different fields. Some of the industries are healthcare, self-driving vehicles, and surveillance and security.

Healthcare#

Computer vision is being used in healthcare for the diagnosis and treatment of patients. It is being used to analyze CT scans, MRIs, and X-rays. Computer vision technology is being used to diagnose cancer, heart diseases, Alzheimer's, respiratory diseases, and many other hidden diseases. Computer vision is also being used for remote diagnoses and treatments. It has greatly improved efficiency in the medical field.

Self Driving Vehicles#

Innovation in Computer vision has enabled the automotive industry to improve its self-driving features significantly. Computer vision-based algorithms are used in car sensors to detect objects by vehicles. It has also enabled these vehicles to make real-time decisions based on information from sensors.

Security and Surveillance#

Another application of computer vision is security and surveillance. Computer vision is being used in cameras in public places for security. Facial recognition and object detection are being used for threat detection.

Challenges and Limitations#

No doubt innovation in computer vision has improved AI significantly. It has also raised some challenges and concerns about privacy, ethics, and Interoperability.

Data Privacy#

AI trains on vast amounts of visual data for improved decision-making. This training data is usually taken from surveillance cameras which raises huge privacy concerns. There are also concerns about the storage and collection of users' data because there is no way of knowing which information is being accessed about a person.

Ethics#

Ethics is also becoming a big concern as computer vision is integrated with AI. Pictures and videos of individuals are being used without their permission which goes against ethics. Moreover, it has been seen that some AI models discriminate against people of color. All these ethical concerns need to be addressed properly by taking necessary actions.

Interpretability#

Another important concern of computer vision is interpretability. As AI models continue to evolve, it becomes increasingly difficult to understand how they make decisions. It becomes difficult to interpret if decisions are made based on facts or biases. A new set of tools are required to address this issue.

Conclusion:#

Computer vision is an important field of AI. In recent years there have been many innovations in computer vision that have improved AI algorithms and models significantly. These innovations include image recognition, object detection, semantic segmentation, and video analysis. Due to all these innovations computer vision has become an important part of different fields.

Some of these fields are healthcare, robotics, self-driving vehicles, and security and surveillance. There are also some challenges and concerns which need to be addressed.

CV APIs are growing in popularity because they let developers build smart apps that read, recognize, and analyze visual data from photos and videos. As a consequence, the CV API market is likely to expand rapidly in the coming years to meet the rising demand for these sophisticated applications across a wide range of sectors.

According to MarketsandMarkets, the computer vision market will grow from $10.9 billion in 2019 to $17.4 billion in 2024, with a compound annual growth rate (CAGR) of 7.8 percent. The market for CV APIs is projected to be worth billions of dollars by 2030, continuing the upward trend seen since 2024.

What is Computer Vision?#

Computer Vision is a branch of artificial intelligence (AI) that aims to offer computers the same visual perception and understanding capabilities as humans. Computer Vision algorithms use machine learning and other cutting-edge methods to analyze and interpret visual input. These algorithms can recognize patterns, recognize features, and find anomalies by learning from large picture and video datasets.

The significance of Computer Vision as an indispensable tool in various industries continues to grow, with its applications continually expanding.

Below given are just a few examples of where computer vision is employed today:

• Automatic inspection in manufacturing applications
• Assisting humans in identification tasks
• Controlling robots
• Detecting events
• Modeling objects and environments
• Navigation
• Medical image processing
• Autonomous vehicles
• Military applications

Benefits of Using Computer Vision in Cloud Computing#

Cloud computing is a common platform utilized for scalable and flexible image and video processing by implementing Computer Vision APIs.

Image and Video Recognition:#

Using cloud-based Computer Vision APIs enables the analysis and recognition of various elements within images and videos, such as objects, faces, emotions, and text.

Augmented Reality:#

The utilization of Computer Vision APIs in augmented reality (AR) applications allows for the detection and tracking of real-world objects, which in turn facilitates the overlaying of virtual content.

Security:#

Computer Vision APIs, such as face recognition and object detection, may be used in security systems to detect and identify potential security risks.

Real-time Analytics:#

Real-time data processing is made possible by cloud-based Computer Vision APIs, resulting in quicker decision-making and an enhanced user experience.

Automated Quality Control:#

The automation of quality control processes and the identification of product defects can be achieved in manufacturing and production settings by utilizing Computer Vision APIs.

Visual Search:#

Visual search capabilities can be facilitated through the application of Computer Vision APIs, allowing for the upload of images to search for products in e-commerce and other related applications.

Natural Language Processing:#

Computer Vision APIs can be utilized alongside natural language processing (NLP) to achieve a more comprehensive understanding of text and images.

Way of Using Computer Vision on the Edge#

Certain conditions must be satisfied before computer vision may be deployed on edge. Computer vision often necessitates an edge device with a GPU or VPU (visual processing unit). Edge devices are often associated with IoT (Internet of Things) devices. However, a computer vision edge device might be any device that can interpret visual input to assess its environment.

The next phase of migration is application configuration. Having the program downloaded directly from the Cloud is the quickest and easiest method.

Once the device has been successfully deployed, it may stop communicating with the Cloud and start analyzing its collected data. The smartphone is an excellent example of a device that satisfies the requirements and is likely already known to most people.

Mobile app developers have been inadvertently developing on the Edge to some extent. Building sophisticated computer vision applications on a smartphone has always been challenging, partly due to the rapid evolution of smartphone hardware.

For instance, in 2021, Qualcomm introduced the Snapdragon 888 5G mobile platform, which will fuel top-of-the-line Android phones. This processor delivers advanced photography features, such as capturing 120 images per second at a resolution of 12 megapixels.

This processor provides advanced photography features, such as capturing 120 images per second at a resolution of 12 megapixels.

An edge device's power enables developers to build complicated apps that can run directly on the smartphone.

Beyond mobile phones, there are more extensive uses for computer vision on Edge. Computer vision at the border is increasingly used in many industries, especially manufacturing. Engineers can monitor the whole process in near real-time due to software deployed at the Edge that allows them to do so.

Real-time examples#

The following is an overview of some of the most well-known Computer Vision APIs and the services they provide:

1. Google Cloud Vision API:#

Images and videos can be recognized, OCR can be read, faces can be identified, and objects can be tracked with the help of Google's Cloud Vision API, a robust Computer Vision API. It has a solid record for accuracy and dependability and provides an easy-to-use application programming interface.

2. Amazon Rekognition:#

Other well-known Computer Vision APIs include Amazon's Rekognition, which can recognize objects, faces, texts, and even famous people. It's renowned for being user-friendly and scalable and works well with other Amazon Web Services.

3. Microsoft Azure Computer Vision API:#

Image and video recognition, optical character recognition, and face recognition are just a few of the capabilities provided by the Microsoft Azure Computer Vision API. It has a stellar history of clarity and speed and supports many languages.

4. IBM Watson Visual Recognition:#

Image recognition, face recognition, and individualized training are only some of the capabilities the IBM Watson Visual Recognition API provides. It may be customized to meet specific needs and works seamlessly with other IBM Watson offerings.

5. Clarifai:#

In addition to custom training and object detection, image and video identification are just some of the popular Computer Vision API capabilities offered by Clarifai. It has a solid record for accuracy and simplicity, including an accessible application programming interface.

Conclusion#

In conclusion, AI's popularity has skyrocketed in the recent past. Companies that have already adopted AI are looking for ways to improve their processes, while those that still need to are likely to do so shortly.

Computer vision, a cutting-edge subfield of artificial intelligence, is more popular than ever and finds widespread application.

Ways BFSI can thrive in dynamic technological advancements#

The banking, financial services, and insurance (BFSI) sector are facing unprecedented challenges as technological advancements continue to disrupt the industry. From digital transformation to data analytics, cybersecurity to partnerships, the BFSI sector must adapt to stay competitive.

In this article, we will explore ways in which BFSI companies can thrive in the face of these challenges. The key way that BFSI companies can thrive in the face of dynamic technological advancements is by embracing digital transformation.

Using AI, Machine Learning, Big Data, and Cloud Computing#

This means investing in technologies such as artificial intelligence (AI), machine learning, blockchain, Big Data, and cloud computing to improve operations and customer experience.

For example, using AI-powered chatbots can improve customer service and reduce costs for banks, while blockchain technology can increase transparency and security for financial transactions. By leveraging these technologies, BFSI companies can improve efficiency, reduce costs, and gain a competitive edge.

Using Data Analytics#

Another good option for BFSI companies to thrive in a rapidly changing technological landscape is by leveraging data analytics. By analysing data based on customer behaviour, market trends, and business performance, BFSI companies can gain valuable insights that can help them identify new opportunities and make more accurate decisions.

For example, data analytics can help insurers identify fraudulent claims, while banks can use data to identify potential customers for loans. By using data analytics, BFSI companies can improve the effectiveness of their marketing and sales efforts, as well as reduce risks.

Role of Cybersecurity#

Cybersecurity is also crucial for BFSI companies as they increasingly rely on digital technologies. With the increasing use of digital technologies, BFSI companies must prioritize cybersecurity to protect customer data, prevent cyber-attacks, and protect customers from any frauds or scams. This means investing in security protocols, firewalls, and intrusion detection systems, as well as training employees on best practices for data security. By doing so, BFSI companies can protect their customer's sensitive information and prevent costly data breaches.

Partnerships and Alliances#

It is important for BFSI companies to build partnerships and collaborations with tech giants to have their technological advancement. By working with fintech firms, tech companies, and other partners, BFSI companies can gain access to the newest technologies and services, as well as new markets.

For example, partnering with a fintech firm can help a bank offer new digital services to customers while collaborating with a tech company can help an insurer develop new products and services. By building these partnerships and collaborations, BFSI companies can stay ahead of the curve in an ever-changing landscape.

Innovations#

Innovation is also a key element for BFSI to thrive in the dynamic technological advancements. Developing new products and services that meet the changing needs of customers is critical for staying competitive.

For example, a bank could develop a new mobile app that allows customers to deposit checks using their smartphones, while an insurer could develop a new policy that covers damages from cyber attacks. By developing new products and services, BFSI companies can attract new customers and retain existing ones. These small innovations could make a huge impact on their overall market.

Employee Training and Development#

Investing in employee training and development is crucial for BFSI companies to thrive in a rapidly changing technological landscape. By providing employees with the skills and knowledge needed to work with new technologies, BFSI companies can ensure they have the talent they need to stay competitive.

For example, training employees in data analytics can help them make more accurate decisions, while training in cybersecurity can help them protect customer data. By investing in employee training and development, BFSI companies can ensure that they have the workforce they need to succeed in a dynamic technological landscape.

Building a Strong Digital Ecosystem#

BFSI companies should build a strong digital ecosystem by integrating various technologies and services to create a seamless customer experience. This includes leveraging technologies such as biometrics, natural language processing, and machine learning. It will make the BFSI ecosystem strong and improve the overall customer experience. BFSI can strengthen its security, privacy, and user experience by upgrading its ecosystem digitally.

Identify Emerging Technologies#

BFSI companies should stay updated about emerging technologies such as quantum computing, 5G, and the Internet of Things, and assess how they can be leveraged to improve operations or create new products and services. By adopting emerging digital technologies for services such as mobile banking, online banking, and blockchain, it can improve its customer experience and automate operations.

Digital Identity#

Implementing digital identity solutions to improve security and convenience for customers. Nowadays, we find many fake websites and frauds operating in the name of huge financial companies. Such scammers hunt down customers by spamming them with emails and SMSs. They sell collected data to the 3rd party services for financial gains. Digital identity solutions reduce these scams.

Digital Wallets#

Developing digital wallets to enable customers to store, manage, and transact with digital currency anytime. Supporting contactless payments such as NFC, QR codes, and digital wallets to improve convenience for customers and reduce the risk of fraud.

The BFSI sector is facing unprecedented challenges as technological advancements continue to disrupt the industry. By embracing digital transformation, leveraging data analytics, focusing on cybersecurity, building partnerships and collaborations, developing new products and services, and investing in employee training and development, the BFSI sector could thrive very well.

So the conclusion is like, It's important to note that BFSI companies should also be aware of the regulatory and compliance requirements that come with the adoption of new technologies. They must ensure that their operations and services remain compliant with local and international laws and regulations to avoid any legal issues. To thrive in this dynamic landscape, BFSI companies must take a strategic approach, embracing digital transformation, leveraging data analytics, prioritizing cybersecurity, building partnerships, innovating new products and services, and investing in employee training and development. By doing so, BFSI companies can stay competitive, improve efficiency and customer experience, and ultimately achieve long-term success.

Implementing AI: Artificial Intelligence at Edge is an interesting topic. We will dwell on it a bit more.

This is when things start to get interesting. However, a few extreme situations, such as Netflix, Spotify, and Amazon, are insufficient. Not only is it difficult to learn from extreme situations, but when AI becomes more widespread, we will be able to find best practices by looking at a wider range of enterprises. What are some of the most common issues? What are the most important and effective ways of dealing with them? And, in the end, what do AI-driven businesses look like?

Here are some of the insights gathered to capture, learn from, and share from approximately 2,500 white-collar decision-makers in the United States, the United Kingdom, Germany, India, and China who had all used AI in their respective firms. They were asked questions, and the responses were compiled into a study titled "Adopting AI in Organizations."

Speaking with AI pioneers and newcomers#

Surprisingly, by reaching out on a larger scale, a variety of businesses with varying levels of AI maturity were discovered. They were classified into three groups: AI leaders, AI-followers, and AI beginners, with the AI leaders having completely incorporated AI and advanced analytics in their organizations, as opposed to the AI beginners who are only starting on this road.

The road to becoming AI-powered is paved with potholes that might sabotage your development.

In sum, 99 percent of the decision-makers in this survey had encountered difficulties with AI implementation. And it appears that the longer you work at it, the more difficult it becomes. For example, 75 percent or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more efforts than the other two groups and began 4-5 years ago, said that over 60% of their initiatives had encountered difficulties.

The key follow-up question is, "What types of challenges are you facing?" Do you believe it has something to do with technology? Perhaps you should brace yourself for a slight shock. The major issue was not one of technology. Rather, 91 percent of respondents stated they had faced difficulties in each of the three categories examined: technology, organization, and people and culture. Out of these categories, it becomes evident that people and culture were the most problematic. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. Many respondents, for example, stated that staff was resistant to embracing new ways of working or that they were afraid of losing their employment.

As a result, it should come as no surprise that the most important strategies for overcoming challenges are all related to people and culture. Overall, it is clear that the transition to AI is a cultural one!

A long-term investment in change for Artificial Intelligence at Edge#

But where does this adventure take us? We assume that most firms embarking on an organizational transformation foresee moving from one stable state to a new stable one after a period of controlled turbulence. When we look at how these AI-adopting companies envisage the future, however, this does not appear to be the case!

Conclusion for Artificial Intelligence at Edge:#

To get a sense of what it'll be like to be entirely AI-driven, researchers looked to the AI leaders, who have gone the furthest and may have a better idea of where they're going. This group has already integrated AI into their business or plans to do so by the year 2021. You'd think that after properly implementing and delivering AI inside the organization, they'd be satisfied with their work. They're still not finished. Quite the contrary, they aim to invest much more in AI over the next 18 months and on a far larger scale than previously. The other two groups had far smaller investment plans.

Can an AI-based edge computing platform drive businesses or is that a myth? We explore this topic here._

Introduction#

For a long time, artificial intelligence has been a hot issue. We've all heard successful tales of forward-thinking corporations creating one brilliant technique or another to use Artificial Intelligence technology or organizations that promise to put AI-first or be truly "AI-driven." For a few years now, Artificial Intelligence (AI) has been impacting sectors all around the world. Businesses that surpass their rivals are certainly employing AI to assist in guiding their marketing decisions, even if it isn't always visible to the human eye (Davenport et al., 2019). Machine learning methods enable AI to be characterized as machines or processes with human-like intelligence. One of the most appealing features of AI is that it may be used in any sector. By evaluating and exploiting excellent data, AI can solve problems and boost business efficiency regardless of the size of a company (Eitel-Porter, 2020). Companies are no longer demanding to be at the forefront or even second in their sectors; instead, businesses are approaching this transition as if it were a natural progression.

Artificial Intelligence's (AI-driven) Business Benefits#

Businesses had to depend on analytics researchers in the past to evaluate their data and spot patterns. It was practically difficult for them to notice each pattern or useful bit of data due to the huge volume of data accessible and the brief period in their shift. Data may now be evaluated and processed in real-time thanks to artificial intelligence. As a result, businesses can speed up the optimization process when it comes to business decisions, resulting in better results in less time. These effects can range from little improvements in internal corporate procedures to major improvements in traffic efficiency in large cities (Abduljabbar et al., 2019). The list of AI's additional advantages is nearly endless. Let's have a look at how businesses can benefit:

• A More Positive Customer Experience: Among the most significant advantages of AI is the improved customer experience it provides. Artificial intelligence helps businesses to improve their current products by analyzing customer behavior systematically and continuously. AI can also help engage customers by providing more appropriate advertisements and product suggestions (Palaiogeorgou et al., 2021).

• Boost Your Company's Efficiency: The capacity to automate corporate procedures is another advantage of artificial intelligence. Instead of wasting labor hours by having a person execute repeated activities, you may utilize an AI-based solution to complete those duties instantly. Furthermore, by utilizing machine learning technologies, the program can instantly suggest enhancements for both on-premise and cloud-based business processes (Daugherty, 2018). This leads to time and financial savings due to increased productivity and, in many cases, more accurate work.

• Boost Data Security: The fraud and threat security capabilities that AI can provide to businesses are a major bonus. AI displays usage patterns that can help to recognize cyber security risks, both externally and internally. An AI-based security solution could analyze when specific employees log into a cloud solution, which device they used, and from where they accessed cloud data regularly.

Speaking with AI Pioneers and Newcomers#

Surprisingly, by reaching out on a larger scale, researchers were able to identify a variety of firms at various stages of AI maturity. Researchers split everyone into three groups: AI leaders, AI followers, and AI beginners (Brock and von Wangenheim, 2019). The AI leaders have completely adopted AI and data analysis tools in their company, whilst the AI beginners are just getting started. The road to becoming AI-powered is paved with obstacles that might impede any development. In sum, 99% of the survey respondents have encountered difficulties with AI implementation. And it appears that the more we work at it, the more difficult it becomes. 75% or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more effort than the other two groups and began 4-5 years earlier, had over 60% of their projects encounter difficulties. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. The staff was resistant to embracing new methods of working or were afraid of losing their employment. Considering this, it should be unsurprising that the most important tactics for overcoming obstacles include culture and traditions (Campbell et al., 2019). Overall, it's evident that the transition to AI-driven operations is a cultural one!

The Long-Term Strategic Incentive to Invest#

Most firms that begin on an organizational improvement foresee moving from one stable condition to a new stable one after a period of controlled turbulence (ideally). When developers look at how these AI-adopting companies envision the future, however, this does not appear to be the case. Developers should concentrate their efforts on the AI leaders to better grasp what it will be like to be entirely AI-driven since these are the individuals who've already progressed the most and may have a better understanding of where they're headed. It's reasonable to anticipate AI leaders to continue to outpace rival firms in the future (Daugherty, 2018). Maybe it's because they have a different perspective on the current, solid reality that is forming. The vision that AI leaders envisage is not one of consistency and "doneness" in terms of process. Consider a forthcoming business wherein new programs are always being developed, with the ability to increase efficiency, modify job processing tasks, impact judgment, and offer novel issue resolution. It appears that the steady state developers are looking for will be one of constant evolution. An organization in which AI implementation will never be finished. And it is for this reason that we must start preparing for AI Edge Computing Platform to pave the way for the future.

References#

• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), p.189. Available at: link.
• Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), pp.110–134.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2019). From Data to Action: How Marketers Can Leverage AI. Business Horizons.
• Daugherty, P.R. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), pp.24–42. Available at: link.
• Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics.
• Palaiogeorgou, P., Gizelis, C.A., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., & Christonasis, A.M. (2021). AI: Opportunities and Challenges - The Optimal Exploitation of (Telecom) Corporate Data. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.47–59.

There is a common debate on how Edge Computing Platforms for Anomalies Detection can be used. In this blog, we will cover details about it.

Introduction#

Anomalies are a widespread problem across many businesses, and the telecommunications sector is no exception. Anomalies in telecommunications can be linked to system effectiveness, unauthorized access, or forgery, and therefore can present in a number of telecommunications procedures. In recent years, artificial intelligence (AI) has become more prominent in overcoming these issues. Telecommunication invoices are among the most complicated invoices that may be created in any sector. With such a large quantity and diversity of goods and services available, mistakes are unavoidable. Products are made up of product specifications, and the massive amount of these features, as well as their numerous pairings, gives rise to such diversity (Tang et al., 2020). Goods and services – and, as a result, the invoicing process – are becoming even more difficult under 5G. Various corporate strategies, such as ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and large machine-type communication, are being addressed by service providers. Alongside 5G, the 3GPP proposed the idea of network slicing (NW slice) and the related service-level agreements (SLAs), adding still another layer to the invoicing procedure's complexities.

How Do Network Operators Discover Invoice Irregularities?#

Invoice mistakes are a well-known issue in the telecom business, contributing to invoicing conflicts and customer turnover. These mistakes have a significant monetary and personal impact on service providers. To discover invoice abnormalities, most network operators use a combination of traditional and computerized techniques. The manual method is typically dependent on sampling procedures that are determined by company regulations, availability of materials, personal qualities, and knowledge. It's sluggish and doesn't cover all of the bills that have been created. These evaluations can now use regulation digitization to identify patterns and provide additional insight into massive data sets, thanks to the implementation of IT in business operations (Preuveneers et al., 2018). The constant character of the telecom business must also be considered, and keeping up would imply a slowdown in the introduction of new goods and services to the marketplace.

How AI and Machine Learning Can Help Overcome Invoice Anomaly Detection#

An AI-based system may detect invoicing abnormalities more precisely and eliminate false-positive results. Non-compliance actions with concealed characteristics that are hard for humans to detect are also easier to identify using AI (Oprea and Bâra, 2021). Using the procedures below, an AI system learns to recognize invoice anomalous behavior from a collection of data:

1. Data from invoices is incorporated into an AI system.
2. Data points are used to create AI models.
3. Every instance a data point detracts from the model, a possible invoicing anomaly is reported.
4. The invoice anomaly is approved by a specific domain.
5. The system applies what it has learned from the activity to the data model for future projections.
6. Patterns continue to be collected throughout the system.

Before delving into the details of AI, it's vital to set certain ground rules for what constitutes an anomaly. Anomalies are classified as follows:

• Point anomalies: A single incident of data is abnormal if it differs significantly from the others, such as an unusually low or very high invoice value.
• Contextual anomalies: A data point that is ordinarily regular but becomes an anomaly when placed in a specific context.
• Collective anomalies: A group of connected data examples that are anomalous when viewed as a whole but not as individual values. When many point anomalies are connected together, they might create collective anomalies (Anton et al., 2018).

Implications of AI and Machine Learning in Anomaly Detection#

All sectors have witnessed a significant focus on AI and Machine Learning technologies in recent years, and there's a reason why AI and Machine Learning rely on data-driven programming to unearth value hidden in data. AI and Machine Learning can now uncover previously undiscovered information and are the key motivation for their use in invoice anomaly detection (Larriva-Novo et al., 2020). They assist network operators in deciphering the unexplained causes of invoice irregularities, provide genuine analysis, increased precision, and a broader range of surveillance.

Challenges of Artificial Intelligence (AI)#

The data input into an AI/ML algorithm is only as strong as the algorithm itself. When implementing the invoice anomaly algorithm, it must react to changing telecommunications data. Actual data may alter its features or suffer massive reforms, requiring the algorithm to adjust to these changes. This necessitates continual and rigorous monitoring of the model. Common challenges include a loss of confidence and data skew. Unawareness breeds distrust, and clarity and interpretability of predicted results are beneficial, especially in the event of billing discrepancies (Imran, Jamil, and Kim, 2021).

Conclusion for Anomaly Detection#

Telecom bills are among the most complicated payments due to the complexity of telecommunications agreements, goods, and billing procedures. As a result, billing inconsistencies and mistakes are widespread. The existing technique of manually verifying invoices or using dynamic regulation software to detect anomalies has limits, such as a limited number of invoices covered or the inability to identify undefined problems. AI and Machine Learning can assist by encompassing all invoice information and discovering different anomalies over time (Podgorelec, Turkanović, and Karakatič, 2019). Besides invoice anomalies, a growing number of service providers are leveraging AI and Machine Learning technology for various applications.

References#

• Anton, S.D., Kanoor, S., Fraunholz, D., & Schotten, H.D. (2018). Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set. Proceedings of the 13th International Conference on Availability, Reliability and Security.
• Imran, J., Jamil, F., & Kim, D. (2021). An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability, 13(18), p.10057.
• Larriva-Novo, X., Vega-Barbas, M., Villagrá, V.A., Rivera, D., Álvarez-Campana, M., & Berrocal, J. (2020). Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets. Applied Sciences, 10(10), p.3430.
• Oprea, S.-V., & Bâra, A. (2021). Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering, 94, p.107329.
• Podgorelec, B., Turkanović, M., & Karakatič, S. (2019). A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection. Sensors, 20(1), p.147.
• Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Applied Sciences, 8(12), p.2663.
• Tang, P., Qiu, W., Huang, Z., Chen, S., Yan, M., Lian, H., & Li, Z. (2020). Anomaly detection in electronic invoice systems based on machine learning. Information Sciences, 535, pp.172–186.

Introduction#

Machine Learning-Based Techniques for observation and administration are especially suitable for sophisticated network infrastructure operations. Assume a machine learning (ML) program designed to predict mobile service disruptions. Whenever a network administrator obtains an alert about a possible imminent interruption, they can take bold measures to address bad behaviour before something affects users. The machine learning group, which constructs the underlying data processors that receive raw flows of network performance measurements and store them into such a Machine Learning (ML)-optimized databases, assisted in the development of the platform. The preliminary data analysis, feature engineering, Machine Learning (ML) modeling, and hyperparameter tuning are all done by the research team. They collaborate to build a Machine Learning (ML) service that is ready for deployment (Chen et al., 2020). Customers are satisfied because forecasts are made with the anticipated reliability, network operators can promptly repair network faults, and forecasts are produced with the anticipated precision.

What is Machine Learning (ML) Lifecycle?#

The data analyst and database administrators obtain multiple procedures (Pipeline growth, Training stage, and Inference stage) to establish, prepare, and start serving the designs using the massive amounts of data that are engaged in different apps so that the organisation can take full favor of artificial intelligence and Machine Learning (ML) methodologies to generate functional value creation (Ashmore, Calinescu and Paterson, 2021).

Monitoring allows us to understand performance concerns#

Machine Learning (ML) models are based on numbers, and they tacitly presume that the learning and interpretation data have the same probability model. Basic variables of a Machine Learning (ML) model are tuned during learning to maximise predicted efficiency on the training sample. As a result, Machine Learning (ML) models' efficiency may be sub-optimal when compared to databases with diverse properties. It is common for data ranges to alter over time considering the dynamic environment in which Machine Learning (ML) models work. This transition in cellular networks might take weeks to mature as new facility units are constructed and updated (Polyzotis et al., 2018). The datasets that ML models consume from multiple data sources and data warehouses, which are frequently developed and managed by other groups, must be regularly watched for unanticipated issues that might affect ML model results. Additionally, meaningful records of input and model versions are required to guarantee that faults may be rapidly detected and remedied.

Data monitoring can help prevent machine learning errors#

Machine Learning (ML) models have stringent data format requirements because they rely on input data. Whenever new postal codes are discovered, a model trained on data sets, such as a collection of postcodes, may not give valid forecasts. Likewise, if the source data is provided in Fahrenheit, a model trained on temperature readings in Celsius may generate inaccurate forecasts (Yang et al., 2021). These small data changes typically go unnoticed, resulting in performance loss. As a result, extra ML-specific model verification is recommended.

Variations between probability models are measured#

The steady divergence between the learning and interpretation data sets, known as idea drift, is a typical cause of efficiency degradation. This might manifest itself as a change in the mean and standard deviation of quantitative characteristics. As an area grows more crowded, the frequency of login attempts to a base transceiver station may rise. The Kolmogorov-Smirnov (KS) test is used to determine if two probability ranges are equivalent (Chen et al., 2020).

Preventing Machine Learning-Based Techniques for system engineering problems#

The danger of ML efficiency deterioration might be reduced by developing a machine learning system that specifically integrates data management and model quantitative measurement tools. Tasks including data management and [ML-specific verification] are performed at the data pipeline stage. To help with these duties, the programming group has created several public data information version control solutions. Activities for monitoring and enrolling multiple variations of ML models, as well as the facilities for having to serve them to end-users, are found at the ML model phase (Souza et al., 2019). Such activities are all part of a bigger computer science facility that includes automation supervisors, docker containers tools, VMs, as well as other cloud management software.

Data and machine learning models versioning and tracking for Machine Learning-Based Techniques#

The corporate data pipelines can be diverse and tedious, with separate elements controlled by multiple teams, each with their objectives and commitments, accurate data versioning and traceability are critical for quick debugging and root cause investigation (Jennings, Wu and Terpenny, 2016). If sudden events to data schemas, unusual variations to function production, or failures in intermediate feature transition phases are causing ML quality issues, past and present records can help pin down when the problem first showed up, what data is impacted, or which implication outcomes it may have affected.

Using current infrastructure to integrate machine learning systems#

Ultimately, the machine learning system must be adequately incorporated into the current technological framework and corporate environment. To achieve high reliability and resilience, ML-oriented datasets and content providers may need to be set up for ML-optimized inquiries, and load-managing tools may be required. Microservice frameworks, based on containers and virtual machines, are increasingly widely used to run machine learning models (Ashmore, Calinescu, and Paterson, 2021).

Conclusion for Machine Learning-Based Techniques#

The use of Machine Learning-Based Techniques could be quite common in future communication designs. At this scale, vast amounts of data streams might be recorded and stored, and traditional techniques for assessing better data and dispersion drift could become operationally inefficient. The fundamental techniques and procedures may need to be changed. Moreover, future designs are anticipated to see an expansion in the transfer of computing away from a central approach and onto the edge, closer to the final users (Hwang, Kesselheim and Vokinger, 2019). Decreased lags and Netflow are achieved at the expense of a more complicated framework that introduces new technical problems and issues. In such cases, based on regional federal regulations, data gathering and sharing may be restricted, demanding more cautious ways to programs that prepare ML models in a safe, distributed way.

References#

• Ashmore, R., Calinescu, R. and Paterson, C. (2021). Assuring the Machine Learning Lifecycle. ACM Computing Surveys, 54(5), pp.1–39.
• Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S.A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., Zaharia, M., Zang, R., Zheng, J. and Zumar, C. (2020). Developments in MLflow. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning.
• Hwang, T.J., Kesselheim, A.S. and Vokinger, K.N. (2019). Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine. JAMA, 322(23), p.2285.
• Jennings, C., Wu, D. and Terpenny, J. (2016). Forecasting Obsolescence Risk and Product Life Cycle With Machine Learning. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6(9), pp.1428–1439.
• Polyzotis, N., Roy, S., Whang, S.E. and Zinkevich, M. (2018). Data Lifecycle Challenges in Production Machine Learning. ACM SIGMOD Record, 47(2), pp.17–28.
• Souza, R., Azevedo, L., Lourenco, V., Soares, E., Thiago, R., Brandao, R., Civitarese, D., Brazil, E., Moreno, M., Valduriez, P., Mattoso, M., Cerqueira, R. and Netto, M.A.S. (2019).
• Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. 2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS).
• Yang, C., Wang, W., Zhang, Y., Zhang, Z., Shen, L., Li, Y. and See, J. (2021). MLife: a lite framework for machine learning lifecycle initialization. Machine Learning.

Introduction#

In the field of healthcare technology, we are at the start of a high-tech era. AI technology, cloud-based services, the Internet of Things, and big data have all become popular topics of conversation among healthcare professionals as a way to provide high-quality services to patients while cutting costs. Due to ambitions for global application, the fifth generation of cellular technology, or 5G, has gotten a lot of interest. While the majority of media attention has centered on the promise of "the internet of things," the ramifications of 5G-enabled technologies in health care are yet to be addressed (Zhang and Pickwell-Macpherson, 2019). The adoption of 5G in healthcare is one of the elements that is expected to have a significant impact on patient value. 5G, or fifth-generation wireless communications, would not only provide much more capacity but also be extremely responsive owing to its low latency. 5G opens up a slew of possibilities for healthcare, including remote diagnostics, surgery, real-time surveillance, and extended telemedicine (Thayananthan, 2019). This article examines the influence of 5G technology on healthcare delivery and quality, as well as possible areas of concern with this latest tech.

What is 5G?#

The fifth generation of wireless communication technology is known as 5G. Like the preceding fourth generation, the core focus of 5G is speed. Every successive generation of wireless networks improves on the previous one in terms of speed and capability. 5G networks can deliver data at speeds of up to 10 terabytes per second. Similarly, while older networks generally have a delay of 50 milliseconds, 5G networks have a latency of 1–3 milliseconds. With super-fast connection, ultra-low latency, and extensive coverage, 5G marks yet another step ahead (Carlson, 2020). From 2021 to 2026, the worldwide 5G technology market is predicted to grow at a CAGR of 122.3 percent, reaching $667.90 billion. These distinguishing characteristics of 5G enable the possible change in health care as outlined below.

5G's Importance in Healthcare#

Patient value has been steadily declining, resulting in rising healthcare spending. In addition, there is rising concern over medical resource imbalances, ineffective healthcare management, and uncomfortable medical encounters. To address these issues, technologies such as the Internet of Things (IoT), cloud technology, advanced analytics, and artificial intelligence are being developed to enhance customer care and healthcare efficiency while lowering total healthcare costs (Li, 2019). The healthcare business is likely to see the largest improvements as a result of 5G's large bandwidth, reduced latency, and low-power-low-cost. Healthcare professionals investigated and developed several connected-care use cases, but widespread adoption was hampered by the limits of available telecommunications. High-speed and dependable connections will be critical as healthcare systems migrate to a cloud-native design. High data transfer rates, super-low latency, connection and capacity, bandwidth efficiency, and durability per unit area are some of the distinctive properties of 5G technology that have the potential to assist tackle these difficulties (Soldani et al., 2017). Healthcare stakeholders may reorganize, transition to comprehensive data-driven individualized care, improve medical resource use, provide care delivery convenience, and boost patient value thanks to 5G.

5 ways that 5G will change healthcare#

• Large image files must be sent quickly.
• Expanding the use of telemedicine.
• Improving augmented reality, virtual reality, and spatial computing.
• Remote monitoring that is reliable and real-time.
• Artificial Intelligence

Healthcare systems may enhance the quality of treatment and patient satisfaction, reduce the cost of care, and more by connecting all of these technologies over 5G networks (Att.com, 2017). 5G networks can enable providers to deliver more tailored and preventative treatment, rather than just responding to patients' illnesses, which is why many healthcare workers joined providers during the first round.

Challenges#

As with other advances, many industry professionals are cautious about 5G technology's worldwide acceptance in healthcare, as evidenced by the following significant challenges:

• Concerns about privacy and security - The network providers must adhere to the health - care industry's stringent privacy regulations and maintain end-to-end data protection across mobile, IoT, and connected devices.
• Compatibility of Devices - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. As a result, manufacturers have begun to release 5G-enabled smartphones and other products.
• Coverage and Deployment - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. The present 4G network uses certain frequencies on the radio frequency band, often around 6 GHz; however, such systems are available exclusively in a few nations' metro/urban regions, and telecom carriers must create considerable equipment to overcome this difficulty (Chen et al., 2017).
• Infrastructure - As part of the 5G network needs, healthcare facilities, clinics, and other healthcare providers/organizations will need to upgrade and refresh their infrastructure, apps, technologies, and equipment.

Conclusion#

5G has the potential to revolutionize healthcare as we know it. As we saw during the last epidemic, the healthcare business needs tools that can serve people from all socioeconomic backgrounds. Future improvements and gadgets based on new 5G devices and computers can stimulate healthcare transformation, expand consumer access to high-quality treatment, and help close global healthcare inequities (Thuemmler et al., 2017). For enhanced healthcare results, 5G offers network stability, speed, and scalability for telemedicine, as well as catalyzing broad adoption of cutting-edge technologies like artificial intelligence, data science, augmented reality, and the IoT. Healthcare organizations must develop, test, and deploy apps that make use of 5G's key capabilities, such as ultra-high bandwidth, ultra-reliability, ultra-low latency, and huge machine connections.

References#

• Att.com. (2017). 5 Ways 5G will Transform Healthcare | AT&T Business. [online] Available at: https://www.business.att.com/learn/updates/how-5g-will-transform-the-healthcare-industry.html.
• Carlson, E.K. (2020). What Will 5G Bring? Engineering.
• Chen, M., Yang, J., Hao, Y., Mao, S. and Hwang, K. (2017). A 5G Cognitive System for Healthcare. Big Data and Cognitive Computing, 1(1), p.2.
• Li, D. (2019). 5G and Intelligence Medicine—How the Next Generation of Wireless Technology Will Reconstruct Healthcare? Precision Clinical Medicine, 2(4).
• Soldani, D., Fadini, F., Rasanen, H., Duran, J., Niemela, T., Chandramouli, D., Hoglund, T., Doppler, K., Himanen, T., Laiho, J. and Nanavaty, N. (2017). 5G Mobile Systems for Healthcare. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).
• Thayananthan, V. (2019). Healthcare Management using ICT and IoT-based 5G. International Journal of Advanced Computer Science and Applications, 10(4).
• Thuemmler, C., Gavras, A. and Roa, L.M. (2017). Impact of 5G on Healthcare. 5G Mobile and Wireless Communications Technology, pp. 593-613.
• Zhang, M. and Pickwell-Macpherson, E. (2019). The future of 5G Technologies in healthcare. 5G Radio Technologies Seminar.

Are you familiar with the differences between edge computing and cloud computing? Is edge computing a type of branding for a cloud computing resource, or is it something new altogether? Let us find out!

The speed with which data is being added to the cloud is immense. This is because the growing number of devices in the cloud are centralized, so it must transact the information from where the cloud servers are, hence data needs to travel from one location to another so the speed of data travel is slow. If this transaction starts locally, then the data travels at a shorter distance, making it faster. Therefore, cloud suppliers have combined Internet of Things strategies and technology stacks with edge computing for the best usage and efficiency.

In the following article, we will understand the differences between cloud and edge computing. Let us see what this is and how this technology works.

EDGE COMPUTING#

Edge Computing is a varied approach to the cloud. It is the processing of real-time data close to the data source at the edge of any network. This means applications close to the data generated instead of processing all data in a centralized cloud or a data center. It increases efficiency and decreases cost. It brings the storage and power closer to the device where it is most needed. This distribution eliminates lag and saves a scope for various other operations.

It is a networking system, within which data servers and data processing are closer to the computing process so that the latency and bandwidth problems can be reduced.

Now that we know what the basics of edge computing are, let's dive in a little deeper for a better understanding of terms commonly associated with edge computing:

Latency#

Latency is the delay in contacting in real-time from a remotely located data center or cloud. If you are loading an image over the internet, the time to show up completely is called the latency time.

Bandwidth#

The frequency of the maximum amount of data sent over an Internet connection at a time is called Bandwidth. We refer to the speed of sent and received data over a network that is calculated in megabits per second or MBPS as bandwidth.

Leaving latency and bandwidth aside, we choose edge computing over cloud computing in hard-to-reach locations, where there is limited or no connectivity to a central unit or location. These remote locations need local computing, and edge computing provides the perfect solution for it.

Edge computing also benefits from specialized and altered device functions. While these devices are like personal computers, they are not regular computing devices and perform multiple functions benefiting the edge platform. These specialized computing devices are intelligent and respond to machines specifically.

Benefits of Edge Computing#

• Gathering data, analyzing, and processing is done locally on host devices on the edge of the network, which has the caliber to be completed within a fraction of a second.

• It brings analytical capabilities comparatively closer to the user devices and enhances the overall performance.

• Edge computing is a cheaper alternative to the cloud as data transfer is a lengthy and expensive process. It also decreases the risk involved in transferring sensitive user information.

• Increased use of edge computing methods has transformed the use of artificial intelligence in autonomous driving. Artificial Intelligence-powered and self-driving cars and other vehicles require massive data presets from their surroundings to function perfectly in time. If we use cloud computing in such a case, it would be a dangerous application because of the lag.

• The majority of OTT platforms and streaming service providers like Netflix, Amazon Prime, Hulu, and Disney+ to name a few, create a heavy load on cloud network infrastructure. When popular content is cached closer to the end-users in storage facilities for easier and quicker access. These companies make use of the nearby storage units close to the end-user to deliver and stream content with no lag if one has a stable network connection.

The process of edge computing varies from cloud computing as the latter takes considerably more time. Sometimes it takes up to a couple of seconds to channel the information to the data centers, ultimately resulting in delays in crucial decision-making. The signal latency can translate to huge losses for any organization. So, organizations prefer edge computing to cloud computing which eliminates the latency issue and results in the tasks being completed in fractions of a second.

CLOUD COMPUTING#

A cloud is an information technology environment that abstracts, pools, and shares its resources across a network of devices. Cloud computing revolves around centralized servers stored in data centers in large numbers to fulfill the ever-increasing demand for cloud storage. Once user data is created on an end device, its data travels to the centralized server for further processing. It becomes tiresome for processes that require intensive computations repeatedly, as higher latency hinders the experience.

Benefits of Cloud Computing#

• Cloud computing gives companies the option to start with small clouds and increase in size rapidly and efficiently as needed.

• The more cloud-based resources a company has, the more reliable its data backup becomes, as the cloud infrastructure can be replicated in case of any mishap.

• There is little to no service cost involved with cloud computing as the service providers conduct system maintenance on their own from time to time.

• Cloud enables companies to help cut expenses in operational activities and enables mobile accessibility and user engagement framework to a higher degree.

• Many mainstream technology companies have benefited from cloud computing as a resourceful platform. Slack, an American cloud-based software as a service, has hugely benefited from adopting cloud servers for its application of business-to-business and business-to-consumer commerce solutions.

• Another largely known technology giant, Microsoft has its subscription-based product line ‘Microsoft 365' which is centrally based on cloud servers that provide easy access to its office suite.

• Dropbox, infrastructure as a service provider, provides a service- cloud-based storage and sharing system that runs solely on cloud-based servers, combined with an online-only application.

KEY DIFFERENCES#

• The main difference between edge computing and cloud computing is in data processing within the case of cloud computing, data travel is long, which causes data processing to be slower but in contrast edge computing reduces the time difference in the data processing. It's essential to have a thorough understanding of the working of cloud and edge computing.

• Edge computing is based on processing sensitive information and data, while cloud computing processes data that is not time constrained and uses a lesser storage value. To carry out this type of hybrid solution that involves both edge and cloud computing, identifying one's needs and comparing them against monetary values must be the first step in assessing what works best for you. These computing methods vary completely and comprise technological advances unique to each type and cannot replace each other.

• The centralized locations for edge computing need local storage, like a mini data center. Whereas, in the case of cloud computing, the data can be stored in one location. Even when used as part of manufacturing, processing, or shipping operations, it is hard to co-exist without IoT. This is because everyday physical objects that collect and transfer data or dictate actions like controlling switches, locks, motors, or robots are the sources and destinations that edge devices process and activate without depending upon a centralized cloud.

With the Internet of Things gaining popularity and pace, more processing power and data resources are being generated on computer networks. Such data generated by IoT platforms is transferred to the network server, which is set up in a centralized location.

The big data applications that benefit from aggregating data from everywhere and running it through analytics and machine learning to prove to be economically efficient, and hyper-scale data centers will stay in the cloud. We chose edge computing over cloud computing in hard-to-reach locations, where there is limited connectivity to a cloud-based centralized location setup.

CONCLUSION#

The edge computing and cloud computing issue does not conclude that deducing one is better than the other. Edge computing fills the gaps and provides solutions that cloud computing does not have the technological advancements to conduct. When there is a need to retrieve chunks of data and resource-consuming applications need a real-time and effective solution, edge computing offers greater flexibility and brings the data closer to the end user. This enables the creation of a faster, more reliable, and much more efficient computing solution.

Therefore, both edge computing and cloud computing complement each other in providing an effective response system that is foolproof and has no disruptions. Both computing methods work efficiently and in certain applications, edge computing fills and fixes the shortcomings of cloud computing with high latency, fast performance, data privacy, and geographical flexibility of operations.

Functions that are best managed by computing between the end-user devices and local networks are managed by the edge, while the data applications benefit from outsourcing data from everywhere and processing it through AI and ML algorithms. The system architects who have learned to use all these options together have the best advantage of the overall system of edge computing and cloud computing.

Learn more about different use cases on edge computing-

Condition-based monitoring - An Asset to equipment manufacturers (nife.io)

Computer Vision at Edge is a growing subject with significant advancement in the new age of surveillance. Surveillance cameras can be primary or intelligent, but Intelligent cameras are expensive. Every country has some laws associated with Video Surveillance.

How do Video Analytics companies rightfully serve their customers, with high demand?

Nife helps with this.

Introduction#

The need for higher bandwidth and low latency processing has continued with the on-prem servers. While on-prem servers provide low latency, they do not allow flexibility.

Computer Vision can be used for various purposes such as Drone navigation, Wildlife monitoring, Brand value analytics, Productivity monitoring, or even Package delivery monitoring can be done with the help of these high-tech devices. The major challenge in computing on the cloud is data privacy, especially when images are analyzed and stored.

Another major challenge is spinning up the same algorithm or application in multiple locations, which means hardware needs to be deployed there. Hence scalability and flexibility are the key issues. Accordingly, Computing and Computed Analytics are hosted and stored in the cloud.

On the other hand, managing and maintaining the on-prem servers is always a challenge. The cost of the servers is high. Additionally, any device failure adds to the cost of the system integrator.

Thereby, scaling the application to host computer vision on the network edge significantly reduces the cost of the cloud while providing flexibility of the cloud.

Key Challenges and Drivers of Computer Vision at Edge#

• On-premise services
• Networking
• Flexibility
• High Bandwidth
• Low-Latency

Solution Overview#

Computer Vision requires high bandwidth and high processing, including GPUs. The Edge Cloud is critical in offering flexibility and a low price entry point of cloud hosting and, along with that, offering low latency necessary for compute-intensive applications.

Scaling the application to host on the network edge significantly reduces the camera's cost and minimizes the device capex. It can also help scale the business and comply with data privacy laws, e.g. HIPAA, GDPR, and PCI, requiring local access to the cloud.

How does Nife Help with Computer Vision at Edge?#

Use Nife to seamlessly deploy, monitor, and scale applications to as many global locations as possible in 3 simple steps. Nife works well with Computer Vision.

• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)
  • No difference in application performance (70% improvement from Cloud)
  • Manage and Monitor all applications in a single pane of glass.
  • Update applications and know when an application is down using an interactive dashboard.
  • Reduce CapEx by using the existing infrastructure.

A Real-Life Example of the Edge Deployment of Computer Vision and the Results#

In the current practice, deploying the same application, which needs a low latency use case, is a challenge.

• It needs man-hours to deploy the application.
• It needs either on-prem server deployment or high-end servers on the cloud.

Nife servers are present across regions and can be used to deploy the same applications and new applications closer to the IoT cameras in Industrial Areas, Smart Cities, Schools, Offices, and in various locations. With this, you can monitor foot-fall, productivity, and other key performance metrics at lower costs and build productivity.

Conclusion#

Technology has revolutionized the world, and devices are used for almost all activities to monitor living forms. The network edge lowers latency, has reduced backhaul, and supports flexibility according to the user's choice and needs. We can attribute IoT cameras to scalability and flexibility, which are critical for the device. Hence, ensuring that mission-critical monitoring would be smarter, more accurate, and more reliable.

Want to know how you can save up on your cloud budgets? Read this blog.

AR Mirrors or Smart mirrors, the future of mirrors, is known as the world's most advanced Digital Mirrors. Augmented Reality mirrors are a reality today, and they hold certain advantages amidst COVID-19 as well.

Learn More about how to deploy and scale Smart Mirrors.

Introduction#

AR Mirrors are the future and are used in many places for ease of use for the end-users. AR mirrors are also used in Media & Entertainment sectors because the customers get easy usage of these mirrors, the real mirrors. The AI improves the edge's performance, and the battery concern is eradicated with edge computing.

Background#

Augmented Reality, Artificial intelligence, Virtual reality and Edge computing will help to make retail stores more interactive and the online experience more real-life, elevating the customer experience and driving sales.

Recently, in retail markets, the use of AR mirrors has emerged, offering many advantages. The benefits of using these mirrors are endless, and so is the ability of the edge.

For shoppers to go back to the stores, the touch and feel are the last to focus on. Smart Mirrors bring altogether a new experience of visualizing different garments, how the clothes actually fit on the person, exploring multiple choices and sizes to create a very realistic augmented reflection, yet avoiding physical wear and touch.

About#

We use real mirrors in trial rooms to try clothes and accessories. Smart mirrors have become necessary with the spread of the pandemic.

The mirrors make the virtual objects tangible and handy, which provides maximum utility to the users building on customer experience. Generally, as human nature, the normal mirrors in the real world more often to get a look and feel.

Hence, these mirrors take you to the virtual world, help you with looking at jewellery, accessories and even clothes making the shopping experience more holistic.

Smart Mirrors use an embedded processor with AI. The local processor ensures no lag when the user is using the Mirrors and hence provides an inference closest to the user. While this helps with the inference, the cost of the processor increases.

In order to drive large scale deployment, the cost of mirrors needs to be brought down. Today, AR mirrors have a high price, hence deploying them in retail stores or malls has become a challenge.

The other challenge includes updates to the AR application itself. Today, the System Integrator needs to go to every single location and update the application.

Nife.io delivers by using minimum unit architecture, each connected to the central edge server that can lower the overall cost and help to scale the application on Smart Mirror

Key challenges and drivers of AR Mirrors#

• Localized Data Processing
• Reliability
• Application performance is not compromised
• Reduced Backhaul

Result#

AR Mirrors deliver a seamless user experience with AI. It is a light device that also provides data localization for ease of access to the end-user.

AR Mirrors come with flexible features and can easily be used according to the user's preference.

Here, edge computing helps in reducing hardware costs and ensures that the customers and their end-users do not have to compromise with application performance.

1. The local AI processing moves to the central server.
2. The processor now gets connected to a camera to get the visual information and pass it on to the server.

Since the processing is moved away from the server itself, this helps AR mirrors also can help reduce battery life.

The critical piece here is lag in operations. The end-user should not face any lag, the central server then must have enough processing power and enough isolations to run the operations.

Since the central server with network connectivity is in the control of the application owner and the system integrator, the time spent to deploy in multiple servers is completely reduced.

How does Nife Help with AR Mirrors?#

Use Nife to offload device compute and deploy applications close to the Smart Mirrors.

• Offload local computation
• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Smart Mirrors (40% Cost Reduction)
• Manage and Monitor all applications in a single pane of glass.
• Seamlessly deploy and manage applications ( 5 min to deploy, 3 min to scale)

What's happening with 5G and the 5G networks innovation and rollout? How are these shaping innovation and the world we know? Are you curious? Read More!

We will never forget the year 2020 as the year of the COVID-19 pandemic. We all remember how we witnessed a lengthy lockdown during 2020, and it put all our work on a halt for some time. But we all know that the internet remains one of the best remedies to spend time while at home. We have a 4G network, but there was news that the 5G network would soon become a new normal. Interestingly, even during COVID-19, there were several developments in the 5G network. This article will tell you how the 5G network testing and development stayed intact even during the pandemic.

Innovative Tools That Helped in 5G Testing Even During the Pandemic (Intelligent Site Engineering)#

To continue the 5G testing and deployment even during a pandemic, Telcos used specific innovative tools, the prominent being ISE (Intelligent Site Engineering).

What is Intelligent Site Engineering?#

Intelligent Site Engineering refers to the technique of using laser scanners and drones to design network sites. It is one of the latest ways of network site designing. In this process, they collected every minute detail to create a digital twin of a network site. If the company has a digital twin of a network site, they can operate it anywhere, virtually.

They developed Intelligent Site Engineering to meet the increasing data traffic needs and solve the network deployment problems of the Communication Service Providers (CSPs). This incredible technology enabled the site survey and site design even during the pandemic. We all know that site design and site surveys are vital for the proper installation of a network. But it was not possible to survey the site physically. Therefore, these companies used high technologies to launch and deploy 5G networks even in these lockdown times.

Intelligent Site Engineering uses AI (Artificial Intelligence) and ML (Machine Learning) to quickly and efficiently deliver a network. This helps CSPs to deploy frequency bands, multiple technologies, and combined topologies in one place. This advanced technology marks the transition from the traditional technique of using paper, pen, and measuring tape for a site survey to the latest styles like drones carrying high-resolution cameras and laser scanning devices.

How Does Intelligent Site Engineering Save Time?#

The Intelligent Site Engineering technique saves a lot of time for CSPs. For example, in this digitized version, CSPs take only 90 minutes for a site survey. Previously, they had to waste almost half a day in site surveys using primitive tools in the traditional method. Instead, the engineers can use the time these service providers save for doing other critical work.

Also, this process requires fewer people because of digitalization. This means that it saves the headcount of the workforce and the commuting challenges. Also, it reduces the negative impact on the environment.

What is the Use of Digital Twins Prepared in This Process?#

Using Intelligent Site Engineering, the CSPs replicate the actual site. They copied digital twins through 3D scans and photos clicked from every angle. The engineers then use the copies to get an accurate analysis of the site data. With the highly accurate data, they prepared the new equipment. The best example of digital twins is that CSPs can make a wise decision regarding altering the plan for future networks. Therefore, a digital twin comes in handy, from helping in creating material bills to detailed information about the networking site and related documents.

The technique is helpful for customers as well. For example, through the digital twin, customers can view online documents and sign them. In this way, this advanced technology and innovation enable remote acceptance of network sites even with 5G.

How Did the COVID Pandemic Promote the Digitalization of the 5G Network?#

We all know that the COVID pandemic and the subsequent lockdown put several restrictions on travel. Since no one could commute to the network site, it prepared us to switch to digital methods for satisfying our needs. The result was that we switched to Intelligent Site Engineering for 5G network deployment, bringing in 5G networks innovation.

When physical meetings were restricted, we switched to virtual conversations. Video meetings and conference calls became a new normal during the pandemic. Therefore, communication service providers also used the screen share features to show the clients the network sites captured using drones and laser technology. The image resolution was excellent, and the transition from offline to online mode was successful. Training of the personnel also became digitized.

The best part of this digitalization was that there was no need to have everyone on one site. Using these digital twins and technological tools, anyone can view those designs from anywhere. The companies could share the screen, and the clients could review the site without physical presence.

How Much Efficiency Were the CSPs Able to Achieve?#

When communication service providers were asked about the experience of these new technological tools for network sites, they felt it was better than on-site conversations. They reveal that these online calls help everyone look at the same thing and avoid confusion, which was the biggest problem in on-site meetings. Therefore, this reduces the queries, and teams could complete the deal in less time than offline sales.

The most significant benefit is for the technical product managers. They can now work on online techniques for vertical inspection of assets and sites. In addition, 3D modeling is enhanced, and the ground-level captured images ensure efficiency.

Rounding Up About 5G Networks Innovation:#

The year 2020 was indeed a gloomy year for many of us. But the only silver lining was the announcements of technological advancements like the 5G launch, even during these unprecedented times. The technology advancement enabled us to use this pandemic wisely, and we deployed the 5G network at several places. So, we can say that the innovations remained intact even during the pandemic because of intelligent and relevant technologies. Therefore, it would not be wrong to conclude that technological advancements have won over these challenging times and proved the future.