What is Deployment in Simple Words?#

Deployment is a process that enables you to retrieve and enable code from version control so that it can be made readily available to the public in an automated fashion. It involves delivering applications, modules, updates, and patches from developers to users. The methods used by developers to build, test, and deploy new code impact how quickly a product can respond to changes and the quality of each update.

What is the Use of Deployment?#

Deployment automation allows you to deploy software to testing and production environments with a single push. Automation reduces the risk associated with manual processes in the production environment.

There Are Six Types of Deployment#

1. In-Place Deployment
2. Blue/Green Deployment
3. Canary Deployment
4. Ramped Deployment
5. Shadow Deployment
6. A/B Testing Deployment

What is a Deployment Strategy in Application Deployment?#

A deployment strategy is a technique employed by DevOps teams to launch a new version of a software solution. These strategies cover how network traffic in a production environment is transitioned from the old version to the new version. Based on the firm's specialty, a deployment strategy can influence downtime and operational costs.

When it comes to deploying new resources and code versions into your production environment, automation with minimal service interruption is ideal. A deployment strategy is important because it reduces manual configuration and tremendously improves serviceability, as well as reducing the amount of downtime during a deployment.

1. In-Place Deployments#

An in-place deployment updates the application version without replacing infrastructure components. The previous version of the application on each compute resource is stopped, the latest application is installed, and the new version is started and validated. This method minimizes infrastructure costs and management overhead but can affect application availability during deployment.

The deployment process involves updating the infrastructure with new code and restarting the application.

Once the new version is deployed on every resource, the deployment is complete.

In-place deployments are cheaper but can cause application downtime. Mitigation strategies include staggering deployments and ensuring sufficient resources to handle demand.

2. Blue/Green Deployment#

The blue/green deployment strategy involves creating two independent infrastructure environments. The blue environment contains the previous version, while the green environment holds the new version. Traffic is shifted to the green environment, and the DNS record is updated to point to Green's load balancer.

This strategy allows for quick rollbacks in case of failure but incurs additional costs due to running two environments simultaneously.

3. Canary Deployment#

In canary deployment, the new version is gradually introduced while retaining the old version. For example, 10% of traffic might go to the new version while 90% remains with the old version. This approach helps test the stability of the new version with live traffic.

Canary deployment allows for better performance monitoring and faster rollback but can be slow and time-consuming.

4. Ramped Deployment#

The ramped deployment strategy gradually replaces instances of the old version with the new version one at a time. This method ensures zero downtime and enables performance monitoring.

The rollback process is lengthy, as it involves reverting instances one by one.

5. Shadow Deployment#

In shadow deployment, the new version is deployed alongside the old version, but users cannot access it immediately. Requests sent to the old version are copied to the shadow version to test its handling.

This strategy allows for performance monitoring and stability testing but is complex and expensive to set up.

6. A/B Testing Deployment#

A/B testing deployment involves deploying the new version alongside the old version, but only a subset of users can access the new version. This approach measures the effectiveness of the new functionality based on user performance.

Statistics from A/B testing help developers make informed decisions, but setting up A/B testing requires a sophisticated load balancer and is complex.

Introduction#

Automating the deployment of an application in cloud environments like AWS (Amazon Web Services) and GCP (Google Cloud Platform) can provide a streamlined workflow and reduce errors._

Cloud services have transformed the way businesses work. On the one hand, cloud computing provides benefits like reduced cost, flexibility, and scalability. On the other hand, it introduces new challenges that can be addressed through automation._

Automating Deployment in AWS and GCP#

Deployment of applications and services in a cloud-based system can be complex and time-consuming. Automating deployment in cloud systems like AWS and GCP streamlines the workflow. In this section, we will discuss the benefits of automation, tools available in GCP and AWS, and strategies for automation.

Benefits of Automation in Deployment#

Automating deployment provides many benefits, including:

• Speed: Automation accelerates deployment processes, allowing timely incorporation of changes based on market requirements.
• Consistency: Ensures uniformity across different environments.
• Efficiency: Reduces manual effort, enabling organizations to scale deployment processes without additional labor.

Overview of GCP and AWS Deployment Services#

Google Cloud Platform (GCP) offers several services for automating deployment, including:

• Jenkins and Spinnaker for CI/CD pipelines.
• Google Kubernetes Engine (GKE), Google Cloud Build, Google Cloud Functions, and Google Cloud Deployment Manager for various deployment needs.

Amazon Web Services (AWS) provides several automation services, such as:

• AWS Elastic Beanstalk, AWS CodeDeploy, AWS CodePipeline, AWS CloudFormation, and AWS SAM.
• AWS SAM is used for serverless applications, while AWS CodePipeline facilitates continuous delivery.

Strategies for Automating Deployment#

Effective strategies for automating deployment in cloud infrastructure include:

• Infrastructure as Code (IaC): Manage infrastructure through code, using tools like AWS CloudFormation and Terraform.
• Continuous Integration and Continuous Deployment (CI/CD): Regularly incorporate changes using tools such as Jenkins, Travis CI, and CircleCI.

Best Practices for Automating Deployment#

To ensure effective automation:

• Continuous Integration and Version Control: Build, test, and deploy code changes automatically.
• IaC Tools: Use tools like Terraform for consistent deployments.
• Automated Testing: Identify issues promptly to prevent critical failures.
• Security: Ensure that only authorized personnel can make code changes.

Scaling in AWS and GCP#

Scaling is crucial for maintaining application responsiveness and reliability. Both AWS and GCP offer tools to manage scaling. This section covers the benefits of scaling in the cloud, an overview of scaling services, and strategies for automating scaling.

Benefits of Scaling in Cloud Environments#

Scaling in cloud environments provides:

• Flexibility: Adjust resources according to traffic needs.
• Cost Efficiency: Scale up or down based on demand, reducing costs.
• Reliability: Ensure continuous application performance during varying loads.

Overview of AWS and GCP Scaling Services#

Both AWS and GCP offer tools for managing scaling:

• Auto Scaling: Adjust resource levels based on traffic, optimizing cost and performance.
• Load Balancing: Distribute traffic to prevent downtime and crashes.

Strategies for Automating Scaling#

Key strategies include:

• Auto-Scaling Features: Utilize auto-scaling to respond to traffic changes.
• Load Balancing: Evenly distribute traffic to prevent server overload.
• Event-Based Scaling: Set auto-scaling rules for anticipated traffic spikes.

Best Practices for Automating Scaling#

Best practices for effective scaling automation:

• Regular Testing: Ensure smooth operation of scaling processes.
• IaC and CI/CD: Apply these practices for efficient and consistent scaling.
• Resource Monitoring: Track resources to identify and address issues proactively.

Comparing AWS and GCP Automation#

AWS and GCP offer various automation tools and services. The choice between them depends on:

• Implementation Approach: AWS tends to be more general, while GCP offers more customization.
• Service Differences: For example, AWS Elastic Beanstalk provides a managed CI/CD experience, while GCP's Kubernetes offers container orchestration.

Choosing Between AWS and GCP for Automation#

Both platforms offer robust automation services. The decision to choose AWS or GCP should consider factors such as cost-effectiveness, reliability, scalability, and organizational needs.

Conclusion#

Automating deployment and scaling in cloud environments like AWS and GCP is crucial for efficiency and cost savings. This article explores the benefits, strategies, and tools for automating these processes and provides a comparison between AWS and GCP to help you choose the best solution for your needs.

Watch the video for an easy understanding of the blog!

In a world full of software innovation, delivering apps effectively and promptly is a major concern for most businesses. Many teams have used DevOps techniques, which combine software development and IT operations, to achieve this goal. The two most important techniques are continuous integration (CI) and continuous deployment (CD). In this article, we will discuss these two important techniques in-depth.

An Overview of CI and CD in DevOps#

Modern software development methodologies such as Continuous Integration (CI) and Continuous Delivery/Continuous Deployment (CD) need frequent and efficient incremental code updates. CI uses automated build and testing processes to ensure that changes to the code are reliable before being merged into the repository.

As part of the software development process, the CD ensures that the code is delivered promptly and without problems. In the software industry, the CI/CD pipeline refers to the automated process that enables code changes made by developers to be delivered quickly and reliably to the production environment.

Why is CI/CD important?#

By integrating CI/CD into the software development process, businesses can develop software products fast and effectively. The best delivery method produces a steady stream of new features and problem fixes. It provides a useful way for continuously delivering code to production. As a result, companies could sell their software products more quickly than they used to be able to.

What is the difference between CI and CD?#

Continuous Integration(CI)#

As part of the continuous integration (CI) software development process, developers progressively enhance their code and often test it. This method is automated because of the complexity of the procedure and the volume of the demands. Teams can now develop, test, and deploy their apps regularly and securely. By accelerating the process of making code adjustments, CI gives developers additional time to contribute to the program's progress.

What do you need?#

• To ensure code quality, it is necessary to create automated tests for each new feature, improvement, or bug fix.
• For this purpose, a continuous integration server should be set up to monitor the main repository and execute the tests automatically for every new commit pushed.
• It is recommended that developers merge their changes frequently, at a minimum of once a day.

Continuous Delivery(CD)#

Continuous Delivery (CD) refers to the automated Delivery of finished code to environments such as development and testing. CD provides a reliable and automated approach for delivering code to these environments in a consistent manner.

What do you need?#

• To ensure a smooth and efficient development process, it is essential to have a solid understanding of continuous integration and a comprehensive test suite covering a significant portion of the codebase.
• Deployments should be automated, with manual intervention required only to initiate the process. Once the Deployment is underway, human involvement should not be needed.
• To avoid any negative impact on customers, it is recommended that the team adopts feature flags. This allows incomplete or experimental features to be isolated and prevented from affecting the overall production environment.

Continuous Deployment(CD)#

Continuous Deployment is the natural progression from Continuous Delivery. It involves every change that passes the automated tests being automatically deployed to production, which leads to multiple production deployments.

What do you need?#

• To ensure the highest level of software quality, it is crucial to have a strong testing culture in place. The effectiveness of the test suite will determine the quality of each release.
• As deployment frequency increases, the documentation process should be able to keep up with the pace to ensure that all changes are adequately documented.
• When releasing significant changes, feature flags should be utilized as an integral part of the process. This will enable better coordination with other departments, such as support, marketing, and public relations, to ensure a smooth and effective release.

For most companies not bound by regulatory or other requirements, Continuous Deployment should be the ultimate objective.

CI and CD in DevOps: How does CI/CD relate to DevOps?#

DevSecOps' primary objective is to incorporate security into all stages of the DevOps workflows. Organizations can detect vulnerabilities quickly and make informed decisions about risks and mitigation by conducting security activities early and consistently throughout the software development life cycle (SDLC). In traditional security practices, security is typically only addressed during the production stage, which is incompatible with the faster and more agile DevOps approach.

Consequently, security tools must now seamlessly integrate into the developer workflow and the CI/CD pipeline to keep pace with CI and CD in DevOps and prevent slowing down development velocity.

The CI/CD pipeline is a component of the wider DevOps/DevSecOps framework. For successful implementation and operation of a CI/CD pipeline, organizations require tools that eliminate any sources of friction that can hinder integration and Delivery. Teams need an interconnected set of technologies to enable seamless and collaborative development processes.

What AppSec tools are required for CI/CD pipelines?#

To adopt CI/CD, development teams require technologies to avoid integration and delivery delays. Groups need an integrated toolchain of technologies to allow joint and unhindered development operations. With the help of CI/CD pipelines, new product features may be released much more quickly, making consumers happy and reducing the load on developers.

One of the primary hurdles for development teams using a CI/CD pipeline is effectively dealing with security concerns. Business groups must incorporate security measures without compromising the pace of their integration and delivery cycles. An essential step in achieving this objective is to move security testing to earlier stages in the life cycle. This is particularly vital for DevSecOps organizations that depend on automated security testing to maintain pace with the speed of Delivery.

Using the appropriate tools at the right time minimizes overall DevSecOps friction, accelerates release velocity, and boosts quality and efficiency.

What are the benefits of CI/CD?#

CI/CD offers various benefits to the software development company. Some of the benefits are listed below:

• Continuous delivery enabled by automated testing improves software quality and security, resulting in higher code profitability in production.
• Deployment of CI/CD pipelines greatly improves time to market for new product features, increasing customer satisfaction and relieving the development team's workload.
• The significant increase in delivery speed provided by CI/CD pipelines boosts enterprises' competitiveness.
• Routine task automation allows team members to focus on their core strengths, resulting in superior final results.
• Companies that have successfully deployed CI/CD pipelines can attract top talent by avoiding repetitive processes that are typical in conventional waterfall systems and are frequently dependent on other tasks.

Conclusion#

Implementing CI/CD pipelines is crucial for modern software development practices. By combining continuous integration and deployment, teams can ensure that they deliver software quickly, reliably, and at a high level of quality. The benefits of this approach include faster time to market, better collaboration, and an increased ability to innovate and compete in the market. By investing in the right tools and processes, organizations can achieve their DevOps goals and meet the demands of their customers.

Creating a functional Continuous Integration and Delivery pipeline involves a series of successful events. Still, common issues may arise during the setup and use of the pipeline.

Due to the complexity of a CI/CD pipeline, many common issues can occur. These can range from simple to fix problems to deceptive issues that are difficult to troubleshoot. Many people find these issues can arise quickly and be challenging to resolve. Let us discuss some issues related to CI/CD in finance.

Various Issues with CI/CD in the Finance Sector#

Continuous integration (CI) and continuous deployment (CD) are popular practices in software development that involve continuously integrating code changes and deploying them to a production environment. At the same time, these practices have been widely adopted in many industries. They can present challenges in the finance sector due to the sensitive nature of financial data and the strict regulations that govern it.

1. Security#

One potential issue with CI/CD in finance is security. Financial data is often sensitive and confidential and must be protected from unauthorized access or breaches. However, frequent code deployments in a CI/CD pipeline can increase the risk of vulnerabilities being introduced into the system. This can be mitigated by implementing strict security controls and testing procedures throughout the pipeline. But this can be a significant challenge for organizations that are not well-equipped to handle it.

2. Performance Issues#

The CI/CD pipeline aims to deliver software and code updates quickly through automation. However, if not done properly, it can lead to performance issues in the software. One solution is implementing an automated testing system to detect potential performance issues, such as inefficient code, and alert developers for further evaluation. This can prevent the release of poorly performing software builds to customers.

3. Communication#

When working within a CI/CD pipeline, it is common to collaborate with multiple individuals, potentially divided into teams with specific responsibilities. One of the main challenges in CI/CD is effective communication, particularly when issues arise during software deployment. Clear and timely communication is essential for resolving problems quickly.

Effective communication is crucial in the CI/CD pipeline, as failure to properly convey information, such as an error in an automated build test, can lead to serious consequences. This is just one example of why communication is vital in this field.

4. Complexity of CI/CD#

Finally, organizations may also face challenges with managing the complexity of their CI/CD pipeline. With multiple teams and departments working on different parts of the system, it cannot be easy to coordinate and manage all the different components. This can lead to delays in deployments and an increased risk of errors, which can be costly and time-consuming.

How one can overcome the challenges faced in CI/CD#

Solving the issues associated with CI/CD in the finance sector requires a comprehensive approach that addresses security, compliance, system stability and reliability, and complexity management. By taking a proactive approach and implementing the right measures, organizations can successfully implement CI/CD practices while maintaining the security and compliance of their financial systems. Additionally, Organizations should consider investing in a good CI/CD tool specifically designed to meet the needs of financial institutions to support compliance, security, and risk management.

Implementation of security controls#

First and foremost, organizations must prioritize security in their CI/CD pipeline. This can be achieved by implementing strict security controls and testing procedures throughout the pipeline.

An effective way to maintain a high level of security in your pipeline is to implement a monitoring system that covers all sections and to quickly detect and lock down any irregularity. Additionally, minimizing the amount of sensitive information transmitted through code and using code analysis tools to identify and replace vulnerable sections can also enhance security.

To ensure maximum security in your pipeline, it is important to closely monitor access to all components and keep it as restricted as possible.

Regular performance checking#

To comply with regulations, organizations should perform regular audits and assessments of their CI/CD pipeline to ensure that it complies with the laws and regulations that apply to their industry. They should also seek advice from legal and compliance experts to identify the specific requirements they need to meet.

Performance testing allows for easy and efficient comparison of build performance. It can identify bottlenecks and bugs that can significantly decrease performance. Additionally, load simulation testing should also be a crucial part of the performance testing process. To be effective, it is important to have a robust set of tools for this method.

Implement a strategy to check stability and reliability#

Organizations should implement additional testing and quality assurance procedures to ensure system stability and reliability throughout the pipeline. This can include unit testing, integration testing, and performance testing, as well as monitoring and logging systems to detect and respond to any issues that may arise. Additionally, organizations should implement a rollback plan that allows them to quickly restore the system to a stable state in case of an emergency.

Management of the complexity of their CI/CD pipeline#

Finally, organizations must take steps to manage the complexity of their CI/CD pipeline. This can include implementing a centralized configuration management system and establishing clear communication channels between different teams and departments. By implementing a governance model, organizations can ensure that all teams are working together towards the same goal and that there is a clear chain of command in case of issues or conflicts.

CI/CD pipelines heavily rely on automation, but there are aspects that are not automated, such as communication, collaboration and teamwork. These three factors are crucial for the success of the pipeline, and optimizing communication and transparency is essential for a smooth workflow.

Conclusion#

While CI/CD practices can bring significant benefits to organizations regarding efficiency and speed, they can also present significant challenges in the finance sector. Organizations must take a proactive approach to address these challenges. Some solutions to the problems include implementing strict security controls and testing procedures, complying with regulations, ensuring system stability and reliability, and managing the complexity of the pipeline. With the right approach, organizations can successfully implement CI/CD practices while maintaining the security and compliance of their financial systems.

Introduction#

The multi-cloud management is an innovative solution to increase business effectiveness. Because of the custom-made IT solutions on multi-cloud used by businesses for rapid deployments, it results in greater profitability. The use of multi-cloud by large and medium size organizations is based on the advantages offered by cloud computing. The competitive edge to select from the best cloud solution provider is a unique tool for business growth. The global organizations with maximum workloads gets benefitted from multi-cloud operations. The multi-cloud management offers uniqueness to business organizations and makes their operations reliable and safe. However, a business organization can also get negative impacts from technology. There are pros and cons of multi-cloud computing for organizations moving to multi-cloud infrastructure from private cloud services.

Multi-cloud Migration Pros and Cons#

Businesses always migrate from one technolgical platform to other searching profitability. Cloud based migration is enabling businesses to open up to innovative solutions. Currently, there is an on-demand scope of migrating to multi-cloud architecture. The aim is to get benefitted from the pile of IT solutions available from across the best on the cloud. Businesses are carefully selecting the most competitive cloud management considering pros and cons simultanesouly.

Benefits of Migrating to Multi-Cloud Solutions#

There are various benefits that organizations can drive from multi-cloud management elaborated below:

Rapid Innovation#

• Modern businesses migrating to multi-cloud deployment are seeking innovation at a rapid pace that results in changing branding and scalability.
• The use of multi-cloud management offers limitless solutions to business that improves customer approachability.
• Best outcomes from the selection of best services on multi-cloud gives freedom to choose from the very best.

Risk Mitigation#

• Using the multi-cloud infrastructure the businesses are given a risk-free workability that is generated through an independent copy of the application on the cloud server.
• The use of multi-cloud deployment in case of any disruption ensures that businesses on the multi-cloud computing management are working continuously.

Avoiding Vendor Lock-In#

• This is one of the greater benefits to organizations moving their business onto multi-cloud computing management. The private and public cloud services offer restricted access to the services and capabilities.
• Hence, businesses using public or private cloud services offer a lock-In that does not generate competitiveness of the services. Thus, multi-cloud management and multi-cloud providers effectively render opportunities that enable the business to switch services reducing its dependency.

Lower Latency#

• The use of multi-cloud computing is effective in transferring data from one application to another. Migration of the business to a multi-cloud management platform offers lower latency that enables the application and services to transfer their data at a rapid pace.
• This is directly connected with the application usage and its effectiveness for the user and is an advantage to the business migrating to the multi-cloud service.

Drawbacks of Migrating to Multi-Cloud Solutions#

The following are the drawbacks that businesses had to look into when migrating to the multi-cloud management platform:

Talent Management#

• with the growing conversion of business into multi-cloud computing platforms, organizations are struggling to find the right talent to operate and function effectively on the cloud systems.
• The decision to move to multi-cloud management requires skilled people who know how to work on cloud computing systems. With the increased pace of migration to multi-cloud, there is a shortage in the market for the right talent.

Increased Complexity#

• Adding a multi-cloud management platform into the business results in taking in services from the multi vendors as a part of risk mitigation, but it also adds complexity to the business.

• Handling various operational frameworks of software used by various vendors requires knowledge and training, a level of transparency, and technical know-how.

• The cost of managing a multi-talent team comes at accost along with managing the licensing, compliance, and security of the data.

• Thus, businesses migrating to multi-cloud management need to prepare a comprehensive cloud handling strategy to restrict the operational and financial dead-load.

Security Issues#

• The bitter truth is that realizes migrating to a multi-cloud management platform system is an increased risk to data safety.
• Multi-cloud services are provided by various vendors and thus create a vulnerability of IT risks.
• There is a regular issue of access control and ID verification as reported by users.
• Thus, a multi-cloud infrastructure is more difficult to handle as compared to a private cloud.
• Encryption keys and resource policies, requires multi-layer security because of different vendor accessibility.

It is evident that the use of multi-cloud infrastructure to innovate and grow the business has resulted in large-scale migration of businesses and companies across the globe. Post-pandemic work culture and business strategies also place migrating to multi-cloud as a part of future sustainability. Subsequently, there are issues in migrating to multi-cloud management and seeking multi-cloud services from various vendors. The advantages such as risk mitigation, rapid innovation, and avoiding vendor lock-in are the biggest motivation for businesses to migrate to multi-cloud as compared to the high security risks and need for expertise and its associated cost to hire and retain the talent within an organization are some of the positives. Thus, the future belongs to the multi cloud as the benefit offered are more then negatives.

If your enterprise is looking for a way to save cloud budget, do check out this video!

What should you look for in a Games Deployment Company?#

Let us talk about 5 Things that you should look for in a Games Deployment Company before outsourcing your project.

Planning to make your gaming project as successful as Fortnite or Angry Birds? You should discover a trustworthy team of game designers and developers that are eager to bring it to life. You may considerably extend your company's capabilities by outsourcing tasks to diverse organizations since you have access to global experience. Game development outsourcing services are quickly expanding to alleviate a lack of skills or credentials while also lowering operating expenses.

So, how are you going to pick the "one"?

1. Company's Website, Reputation, and Portfolio#

The first thing you'll notice is the company's website. The way it appears and what it contains and uses the best game development software as well as the existence of a website will impact the search progress. You can learn a lot about the games development company on other websites, but without a great presentation on the website, no business can succeed.

It is critical to investigate the reputation of a game production business. Knowing their reputation entails becoming acquainted with their work history, work experience, reviews, and other client testimonies from past jobs [(Young, 2018)].

The portfolio is the finest approach to test the knowledge, which is described not in words, but in particular initiatives. Keep in mind, however, that the portfolio does not include all of the video game developer's initiatives. Only works that have been approved by the customer may be included in the portfolio. Most gaming initiatives are protected by non-disclosure agreements and cannot be shared with site visitors.

2. Various Game Development Services#

Video game companies provide a variety of game development services, including Android game creation, iPhone/iPad game deployment, HTML5 game deployment, best game development software, and so on [(Kautz et al., 2019)]. There are various mobile game development services available. The greater the breadth of services provided by a games development company, the better for its clientele. That is the only way they can provide their clients with a diverse choice of mobile game development services to meet such clients' needs. As a result, you must determine whether their service meets your needs. It's even better if the video game developer specializes in the service you require!

3. Working Procedure#

Choosing a business that goes over every detail with you will benefit you in the long run, as will using the agile style. Knowing the work process of a games development company is important when selecting one. Clients cannot blame the game's successful delivery on the method. This procedure comprises high-quality performance, a good update mechanism, the best game development software, and simple execution. A successful games development business will provide an overview of each stage of production, keep clients updated while working, and adhere to deadlines. Before assigning your project, go through all of the details with the team.

4. The Company's Technical Expertise#

When discussing your idea with a possible games development company, keep the technical knowledge of the team that will work on it in mind. To keep ahead of their competitors, Video game companies require competent video game developers and always use the best game development software. Before making a decision, a client should assess the developers' expertise in the development stack, frameworks, and game engines such as Unity, Unreal, Cocos2d, HTML5, and so on. Determine whether they have hands-on expertise with various frameworks, stacks, gaming engines, and so on. You should meet with the team that will work on your project one-on-one to better grasp their expertise, working technique, and so on.

5. Cost of Game Development#

The cost of your entire project, along with other factors, is a crucial consideration when selecting a games development business [(Vaudour and Heinze, 2019)]. It is not difficult to link cheap cost to bad quality. As a result, the Video game companies' positive ratings should take precedence over their asking price. Quality should never be sacrificed to save money. However, the fees should be proportionate to the services provided. Clients must deem their costs affordable concerning the high-quality service they are providing. You must have a budget in mind for your application deployment, and it must be compatible with the video game developer with whom you intend to collaborate.

Conclusion#

Outsourcing game development has risks. That should not deter you, though. After all, life is intrinsically dangerous. However, you must exercise caution to maximize your profits while minimizing your costs. With the aid of the aforementioned techniques, you can also demonstrate that ideas dominate the world.

Overall, select a video game developer who cherishes long-term collaboration and goes above and beyond to ensure the video game company's products are effectively realized in your project with full value.

Cloud use is increasing, and enterprises are increasingly implementing easy cloud deployment tactics to cut IT expenses. New digital businesses must prioritize service costs. When organizations first launch digital services, the emphasis is on growth rather than cost. However, as a new service or firm expands, profitability becomes increasingly important. New digital service businesses frequently go public while still losing money. However, attention shifts to how they can begin to increase the top line faster than expenses grow. Creating profitable digital services and enterprises requires having a plan, a cheap cloud alternative and knowing how expenses scale.

Why Cloud Deployment on Nife is profitable?#

[Nife] is a serverless and cost-effective cloud alternative platform for developers that allows enterprises to efficiently manage, launch, and scale applications internationally. It runs your apps near your users and scales computing in locations where your program is most frequently used.

Nife's Hybrid Cloud is constructed in the style of a Lego set. To build a multi-region architecture for your applications over a restricted number of cloud locations, you must understand each component—network, infrastructure, capacity, and computing resources. Manage and monitor the infrastructure as well. This does not affect application performance.

Nife's PaaS Platform enables you to deploy various types of services near the end-user, such as entire web apps, APIs, and event-driven serverless operations, without worrying about the underlying infrastructure. Nife includes rapid, continuous deployments as well as an integrated versioning mechanism for managing applications. To allow your apps to migrate across infrastructure globally, you may deploy standard Docker containers or plug your code directly from your Git repositories. Applications may be deployed in many locations as NIFE is a Multi-Cloud platform in Singapore/US/Middle East. The Nife edge network includes an intelligent load balancer and geo-routing based on rules.

How can Cloud Deployment on Nife drive business growth?#

Here are 7 ways you can use Nife's hybrid cloud to grow your business.

1. Increase your output.#

Easy cloud deployment from Nife improves productivity in various ways. For example, you may use your accounting software to conduct reports to identify which items or services sell the best and which salespeople generate the most income. The instant availability of precise, up-to-date business information and a cheap cloud alternative makes it easier to identify and correct inefficiencies inside your organization [(Asmus, Fattah, and Pavlovski, 2016)].

2. Maintain current business data.#

On NIFE, easy cloud deployment makes it easier than ever to keep data and records from all departments in one place. When a business app connects to the central database, it obtains the most recent version. When a database entry is added or altered, it does not need to be manually transferred across to other databases.

3. Protect your company's data and paperwork.#

The latest cloud data encryption technology on NIFE guarantees that all data transmitted to and from your devices is secure, even if it is intercepted by thieves. This covers all documents and communications, both internal and external.

4. Scale as necessary.#

Before investing in an on-premises IT system, you must be certain that you will use it to its maximum capacity to justify the significant initial expenditure [(Attaran and Woods, 2018)]. It also takes months of preparation and specification. NIFE's easy cloud deployment technology adapts to changing business demands significantly better than traditional IT infrastructure and is far less expensive.

5. More chores should be automated.#

Cloud task automation minimizes employee burdens, providing them with more time to be productive. Productivity software plans out the work that needs to be done in the next days and weeks and informs team members well before anything is due, allowing employees to achieve more while requiring less day-to-day supervision [(Surbiryala and Rong, 2019)].

6. Spend less money.#

Cloud computing eliminates the need for IT infrastructure, hardware, and software. This saves money on power and is a terrific way to demonstrate to your clients that you can be socially responsible while still making more money by using cheap cloud alternatives [(Shah and Dubaria, 2019)].

7. Hire fewer programmers and IT personnel.#

The less equipment you need to maintain on-site, the better. You may get started with Nife's cloud computing by sending an email to their customer care staff.

Conclusion#

The cost of easy cloud deployment is determined by the company you select and the services you require. You must decide which cloud type is ideal for your company, how much data you will save, and why you are transferring to the cloud.

NIFE's Hybrid Cloud Platform is the quickest method to build, manage, deploy, and scale any application securely globally using Auto Deployment from Git. It requires no DevOps, servers, or infrastructure management and it's the cheap cloud alternative and Multi-Cloud platform in Singapore/US/Middle East.

Learn more about Hybrid Cloud Deployment.

We have access to a common pool of computer resources in the cloud (servers, storage, applications, and so on) when we use cloud computing. You just need to request extra resources as needed. Continue reading as we discuss the various types of cloud deployment models and service models to assist you in determining the best option for your company.

What is a cloud deployment model?#

A cloud deployment model denotes a specific cloud environment depending on who controls security, who has access to resources, and whether they are shared or dedicated. The cloud deployment model explains how your cloud architecture will appear, how much you may adjust, and whether or not you will receive services [(Patel and Kansara, 2021)]. The links between the infrastructure and your users are also represented by types of cloud deployment models. Because each type of cloud deployment model may satisfy different organizational goals, you should choose the model that best suits the approach of your institution.

Different Types of Cloud Deployment Models#

The cloud deployment model specifies the sort of cloud environment based on ownership, scalability, and access, as well as the nature and purpose of the cloud [(Gupta, Gupta and Shankar, 2021)]. It defines the location of the servers you're using and who owns them. The cloud deployment model describes the appearance of your cloud infrastructure, what you may alter, and whether you will be provided with services or must design everything yourself.

Types of cloud deployment models are:

Public Cloud Deployment#

Anyone may use the public cloud to access systems and services. Because it is exposed to everybody, the public cloud may be less secure. The public cloud is one in which cloud infrastructure services are made available to the general public or significant industrial organizations over the internet. In this deployment model, the infrastructure is controlled by the organization that provides the cloud services, not by the user.

Private Cloud Deployment#

The private cloud deployment approach is opposed to that of the public cloud. It is a one-on-one setting for a single user (customer). It is not necessary to share your hardware with anyone. The contrast between private and public clouds is in how all of the hardware is handled. In this deployment model of cloud computing, the cloud platform is deployed in a secure cloud environment secured by robust firewalls and overseen by an organization's IT staff.

Hybrid Cloud Deployment#

Hybrid cloud deployment provides the best of both worlds by linking the public and private worlds with a layer of proprietary software. With hybrid cloud deployment, you may host the app in a secure environment while benefiting from the cost benefits of the public cloud. In this, organizations can migrate data and applications between clouds by combining two or more cloud deployment strategies. The hybrid cloud deployment is also popular for 'cloud bursting.' It means that if a company operates an application on-premises, but it experiences a high load, it might explode onto the public cloud.

Community Cloud Deployment#

It enables a collection of businesses to access systems and services. It is a distributed system formed by combining the services of many clouds to meet the special demands of a community, industry, or enterprise. The community's infrastructure might be shared by organizations with similar interests or duties. In this deployment model of cloud computing, cloud deployment is often handled by a third party or a collaboration of one or more community organizations.

Cloud Computing Service Models#

Cloud computing enables the delivery of a variety of services defined by roles, service providers, and user firms. The following are major categories of cloud deployment models and services:

Infrastructure as a Service (IaaS)#

IaaS refers to the employment and use of a third-party provider's IT Physical Infrastructure (network, storage, and servers) [(Malla and Christensen, 2019)]. Users can access IT resources via an internet connection because they are hosted on external servers.

Platform as a Service (PaaS)#

PaaS provides for the outsourcing of physical infrastructure as well as the software environment, which includes databases, integration layers, runtimes, and other components.

Software as a Service (SaaS)#

SaaS is delivered through the internet and does not require any prior installation. The services are available from anywhere in the world for a low monthly charge.

Conclusion#

Over time, the cloud has changed drastically. It was initially only an unusual choice with a few modifications. It is available in a variety of flavors, and you can even establish your Private cloud deployment or Hybrid Cloud deployment in your data center. Each deployment model of cloud computing offers a unique offering that may considerably boost your company's worth. You may also change your Cloud deployment model as your needs change.

Organizations continue to build new apps on the cloud or move current applications to the cloud. A company that adopts cloud technologies and/or selects cloud service providers (CSPs) and services or applications without first thoroughly understanding the hazards associated exposes itself to a slew of commercial, economic, technological, regulatory, and compliance hazards. In this blog, we will learn about the hazards of application deployment, Cloud Deployment, Deployment in Cloud Computing, and Cloud deployment models in cloud computing.

What is Cloud Deployment?#

Cloud computing is a network access model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or interaction from service providers [(Moravcik, Segec and Kontsek, 2018)].

Essential Characteristics:#

1. On-demand self-service
2. Broad network access
3. Resource pooling
4. Rapid elasticity
5. Measured service

Service Models:#

1. Software as a service (SaaS)
2. Platform as a service (PaaS)
3. Infrastructure as a service (IaaS)

Deployment Models:#

1. Private Cloud
2. Community cloud
3. Public cloud
4. Hybrid cloud

Hazards of Application Deployment on Clouds#

At a high level, cloud environments face the same hazards as traditional data centre settings; the threat landscape is the same. That is, deployment in cloud computing runs software, and software contains weaknesses that attackers aim to exploit.

1. Consumers now have less visibility and control.

When businesses move assets/operations to the cloud, they lose visibility and control over those assets/operations. When leveraging external cloud services, the CSP assumes responsibility for some rules and infrastructure in Cloud Deployment.

2. On-Demand Self-Service Makes Unauthorized Use Easier.

CSPs make it very simple to add Cloud deployment models in cloud computing. The cloud's on-demand self-service provisioning features enable an organization's people to deploy extra services from the agency's CSP without requiring IT approval. Shadow IT is the practice of employing software in an organisation that is not supported by the organization's IT department.

3. Management APIs that are accessible through the internet may be compromised.

Customers employ application programming interfaces (APIs) exposed by CSPs to control and interact with cloud services (also known as the management plane). These APIs are used by businesses to provide, manage, choreograph, and monitor their assets and people. CSP APIs, unlike management APIs for on-premises computing, are available through the Internet, making them more vulnerable to manipulation.

4. The separation of several tenants fails.

Exploiting system and software vulnerabilities in a CSP's infrastructure, platforms, or applications that allow multi-tenancy might fail to keep tenants separate. An attacker can use this failure to obtain access from one organization's resource to another user's or organization's assets or data.

5. Incomplete data deletion

Data deletion threats emerge because consumers have little insight into where their data is physically housed in the cloud and a limited capacity to verify the secure erasure of their data. This risk is significant since the data is dispersed across several storage devices inside the CSP's infrastructure in a multi-tenancy scenario.

6. Credentials have been stolen.

If an attacker acquires access to a user's cloud credentials, the attacker can utilise the CSP's services such as deployment in cloud computing to provide new resources (if the credentials allow provisioning) and target the organization's assets. An attacker who obtains a CSP administrator's cloud credentials may be able to use them to gain access to the agency's systems and data.

7. Moving to another CSP is complicated by vendor lock-in.

When a company contemplates shifting its deployment in cloud computing from one CSP to another, vendor lock-in becomes a concern. Because of variables such as non-standard data formats, non-standard APIs, and dependency on one CSP's proprietary tools and unique APIs, the company realises that the cost/effort/schedule time required for the transition is substantially more than previously estimated.

8. Increased complexity puts a strain on IT staff.

The transition to the cloud can complicate IT operations. To manage, integrate, and operate in Cloud deployment models in cloud computing, the agency's existing IT employees may need to learn a new paradigm. In addition to their present duties for on-premises IT, IT employees must have the ability and skill level to manage, integrate, and sustain the transfer of assets and data to the cloud.

Conclusion

It is critical to note that CSPs employ a shared responsibility security approach. Some features of security are accepted by the CSP. Other security concerns are shared by the CSP and the consumer. Finally, certain aspects of security remain solely the consumer's responsibility. Effective Cloud deployment models in cloud computing and cloud security are dependent on understanding and fulfilling all customs duties. The inability of consumers to understand or satisfy their duties is a major source of security issues in Cloud Deployment.

Introduction#

5G has been substantially implemented, and network operators now have a huge opportunity to monetize new products and services for companies and customers. Network slicing is a critical tool for achieving customer service and assured reliability. Ericsson has created the most comprehensive network slicing platform, comprising 5G Radio Access Networks (RAN) slicing, enabling automatic and quick deployment of services of new and creative 5G use scenarios, using an edge strategy (Subedi et al., 2021). Ericsson 5G Radio Access Networks (RAN) Slicing has indeed been released, and telecom companies are enthusiastic about the possibilities of new 5G services. For mobile network operators, using system control to coordinate bespoke network slices in the personal and commercial market sectors can provide considerable income prospects. Ericsson provides unique procedures to ensure that speed and priority are maintained throughout the network slicing process. Not only do they have operational and business support systems (OSS/BSS), central, wireless, and transit systems in their portfolio, but they also have complete services like Network Support and Service Continuity (Debbabi, Jmal and Chaari Fourati, 2021).

What is 5G Radio Access Networks (RAN) Slicing?#

The concept of network slicing is incomplete without the cooperation of communication service providers. It assures that the 5G Radio Access Networks (RAN) Slicing-enabled services are both dependable and effective. Carriers can't ensure slice efficiency or meet service contracts unless they have network support and service continuity. Furthermore, if carriers fail to secure slice performance or meet the service-level agreement, they may face punishment and the dangers of losing clients (Mathew, 2020). Ericsson 5G Radio Access Networks (RAN) Slicing provides service operators with the unique and assured quality they have to make the most of their 5G resources. The novel approach was created to improve end-to-end network slicing capabilities for radio access network managing resources and coordination. As a consequence, it constantly optimizes radio resource allocation and priority throughout multiple slices to ensure service-level commitments are met. This software solution, which is based on Ericsson radio experience and has a flexible and adaptable design, will help service providers to satisfy expanding needs in sectors such as improved broadband access, network services, mission-critical connectivity, and crucial Internet of Things (IoT) (Li et al., 2017).

Ericsson Network Support#

Across complex ecosystems, such as cloud networks, Network Support enables data-driven fault isolation, which is also necessary to efficiently manage the complexity in [5G systems]. To properly manage the complexity of 5G networks, Ericsson Network Support offers data-driven fault isolation. This guarantees that system faults are quickly resolved and that networks are reliable and robust. Software, equipment, and replacement parts are divided into three categories in Network Support. By properly localizing defects and reducing catastrophic occurrences at the solution level, Ericsson can offer quick timeframes and fewer site visits. Ericsson also supports network slicing by handling multi-vendor ecosystem fault separation and resolving complications among domains (Zhang, 2019). Data-driven fault isolation from Ericsson guarantees the quick resolution of connection problems, as well as strong and effective networks, and includes the following innovative capabilities:

• Ericsson Network Support (Software) provides the carrier's software platform requirements across classic, automated, and cloud-based services in extremely sophisticated network settings. It prevents many mishaps by combining powerful data-driven support approaches with strong domain and networking experience.

• Ericsson Hardware Services provides network hardware support. Connected adds advanced technologies to remote activities, allowing for quicker problem identification and treatment. It integrates network data with past patterns to provide service personnel and network management with relevant real-time information. It is feasible to pinpoint errors with greater precision using remote scans and debugging.

• The Spare Components Management solution gives the operator's field engineers access to the parts they need to keep the network up and running (Subedi et al., 2021). Ericsson will use its broad network of logistical hubs and local parts depots to organize, warehouse, and transport the components.

Ericsson Service Continuity#

To accomplish 5G operational readiness, Service Continuity provides AI-powered, proactive assistance, backed by tight cooperation and Always-On service. Advanced analytical automation and reactive anticipatory insights provided by Ericsson Network Intelligence allow Service Continuity services. It focuses on crucial functionality to help customers reach specified business objectives while streamlining processes and ensuring service continuity (Katsalis et al., 2017). It is based on data-driven analysis and worldwide knowledge that is given directly and consists of two services:

• Ericsson Service Continuity for 5G: Enables the clients' networks to take remedial steps forward in time to prevent end-user disruption, allowing them to move from responsive to proactively network services.

• Ericsson Service Continuity for Private Networks is a smart KPI-based support product for Industry 4.0 systems and services that is targeted to the unique use of Private Networks where excellent performance is critical (Mathew, 2020).

Conclusion for 5G Network Slicing

Network slicing will be one of the most important innovations in the 5G network area, transforming the telecommunications sector. The 5G future necessitates a network that can accommodate a diverse variety of equipment and end customers. Communication service providers must act quickly as the massive network-slicing economic potential emerges (Da Silva et al., 2016). However, deciding where to begin or where to engage is difficult. Ericsson's comprehensive portfolio and end-to-end strategy include Network Support and Service Continuity services. Communication service providers across the world would then "walk the talk" for Network Slicing in the 5G age after incorporating them into their network operations plan.

References#

• Da Silva, I.L., Mildh, G., Saily, M. and Hailu, S. (2016). A novel state model for 5G Radio Access Networks. 2016 IEEE International Conference on Communications Workshops (ICC).
• Debbabi, F., Jmal, R. and Chaari Fourati, L. (2021). 5G network slicing: Fundamental concepts, architectures, algorithmics, project practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20).
• Katsalis, K., Nikaein, N., Schiller, E., Ksentini, A. and Braun, T. (2017). Network Slices toward 5G Communications: Slicing the LTE Network. IEEE Communications Magazine, 55(8), pp.146–154.
• Li, X., Samaka, M., Chan, H.A., Bhamare, D., Gupta, L., Guo, C. and Jain, R. (2017). Network Slicing for 5G: Challenges and Opportunities. IEEE Internet Computing, 21(5), pp.20–27.
• Mathew, A., 2020, March. Network slicing in 5G and the security concerns. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 75-78). IEEE.
• Subedi, P., Alsadoon, A., Prasad, P.W.C., Rehman, S., Giweli, N., Imran, M. and Arif, S. (2021). Network slicing: a next-generation 5G perspective. EURASIP Journal on Wireless Communications and Networking, 2021(1).
• Zhang, S. (2019). An Overview of Network Slicing for 5G. IEEE Wireless Communications, [online] 26(3), pp.111–117.

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.