What is the Role of Machine Learning in DevOps?

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Devops

Artificial intelligence (AI) and machine learning (ML) are advanced technologies in the IT world. It is a perfect combination and helps in both the private and professional lives of individuals. If your field is related to eCommerce, software or a mobile application.

There is a golden chance for everyone; whether you have started your career or running a business. But it is important to understand the fundamentals of AI, ML, and other emerging technologies in today’s world.

AI and ML also help in DevOps, both perform their task with efficiency and to enhance the performance of teams and help to grow business. Developers can take the advantages of AI and ML to their benefits.

A company that desires to automate DevOps, needs to build a custom AI/ML layer. However, the primary step is to determine a robust DevOps infrastructure. Once the infrastructure is made, AI/ML is often applied for increased efficiency. AI/ML helps DevOps teams to increase creativity and innovation by allowing teams to manage the quantity, speed, and variability of knowledge. Hence, it increases the automated enhancement and a rise in the DevOps team’s efficiency.

DevOps is a well-established set of practices based around Continuous Integration, Continuous Deployment (CI/CD) and infrastructure. DevOps tools are famous for making software development projects to speed up their work in developments. There is an algorithm that performs operations & procedures, allowing those tools in DevOps to execute effectively.

Artificial Intelligence, Machine Learning are the driving forces for DevOps

The term DevOps is basically AI-driven, it helps to manage the huge capacity of information and computation in everyday operations. AI has the potential to become the first tool for assessing, computing and decision-making procedures in DevOps. AI can change how DevOps teams develop, deliver, deploy and organize applications to enhance their performances and the business operations of DevOps as well.

The combination of Machine Learning (ML) and DevOps are as follows

IT Operations Analytics (ITOA)
Predictive Analytics (PA)
Artificial Intelligence (AI)
Algorithmic IT Operations (AIOps)

Machine Learning is the application of AI and a mixture of algorithms. Nowadays, ML has become very fashionable in software products and applications. ML is the next generation of Automation. DevOps with the automation enables a rapid SDLC, almost like automation, ML uniquely handles the quantity, velocity, and sort of data that is generated using the next generation of automation.

Key points for applying Machine Learning to DevOps

Greater implementation efficiency – AI helps to reduce complexity by assessing human intelligence and convert them efficiently.
Effective use of resources – AI is responsible to automate routine and repeatable tasks, which minimizes the complexity of managing resources to some extent.
Enhanced data accessibility- The whole data may be a critical thing for DevOps teams; AI can collect the data from multiple sources & make it reliable and useful for further procedures.
Managing production – Machine Learning helps in analyzing an application in production, especially for larger data volumes and transactions. The DevOps teams use ML to research general patterns including resource utilization, volume and so on.
Managing alert storms – The practical application of ML helps in managing the large flood of alerts, which creates an issue while working.
Troubleshooting analytics – Nowadays, ML plays a great role in analytics. These tools generally process & identify threats. Although, other automation tools are also used ML to boost a ticket, alert operations and so on.
Preventing production failures – ML helps operations to avoid issues to work faster with the quick response time.
Analyzing business impact – ML systems can detect good and bad patterns by analyzing the metrics of the user and ready to help developers while facing issues to solve bugs in applications.

Organizations must help AI and ML to optimize DevOps

AI can help in managing complex data pipelines and make modules that will help in the application development process. By the next few years AI and ML will be leading the digital transformation.

However, implementing AI and ML for DevOps also presents a variety of challenges for organizations of all sizes. To profit from AI and ML technologies, a customized DevOps stack is required. If you have an open-source project like the Fabric for Deep Learning (FfDL) and Model Asset eXchange then these tools can lessen the burden on the DevOps team. These technologies can help to maintain the DevOps process more efficiently.

Application of AI and ML may result in true ROI for a corporation by optimizing DevOps operations, making IT operations more responsive. They will also improve the productivity of the team and play a crucial role in filling the gap between human and large data.

Conclusion

Businesses are under the pressure of clients to satisfy their changing demands and enhance their performance. However, it difficult for several companies to use AI and ML due to the complexity involved. To acknowledge any benefit with AI and DevOps, a proper mindset is required.

Source: The Role of Machine Learning in DevOps

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