Developers
August 21, 2020

How AI Is Impacting DevOps

AI is transforming DevOps, helping it achieve its full potential.

Artificial intelligence (AI) is one of the hottest topics in the tech industry. Depending on who you ask, AI is poised to either be one of the most important technological and societal advances in history, or one of the most dangerous pursuits to date.

Whatever AI’s role in the big picture, there is no doubt it is already transforming multiple industries and disciplines. One such example is DevOps.

What Is DevOps

As the name implies, DevOps is the intersection of Development and IT Operations. While it would seem, on the surface, that these two departments should work seamlessly together, the reality is often a far different story.

Developers and operations personnel often approach problems from two entirely different perspectives, which can sometimes lead to a breakdown in communication over the best way to approach things. Similarly, there are times when it’s desirable to have a measure of separation between development and operations, especially when it comes to some security issues.

DevOps is designed to help break down these barriers and help the two departments work together as efficiently as possible. DevOps is the natural progression of the Agile and Lean approaches. Lean focuses primarily on the bare minimum that’s required, what adds value, and ignores everything else. Agile, is designed to break down old methods and processes, especially those that are more about doing things a certain way than being productive.

The Agile Manifesto says it best:

We are uncovering better ways of developing

software by doing it and helping others do it.

Through this work we have come to value:

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiation

Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

In many ways, DevOps blends these two philosophies, focusing only on what adds value and removing any obstacles to that. Similarly, DevOps ignores established convention in favor of true productivity. To aid in these goals, rather than monolithic updates, DevOps emphasizes frequent, incremental improvements, backed up by robust testing.

How AI Is Helping DevOps

One of the biggest ways AI is helping DevOps is in the realm of communication. DevOps emphasizes frequent communication between the departments, but putting it into practice can be overwhelming, especially in larger organizations. AI chatbots can be a valuable tool to ease this process, facilitating communication and keeping up with the volume of information.

Another significant way AI is assisting DevOps is in the development process. AI has reached the point where it can streamline development, through code analysis and automatic completion. Similarly, AI has proven to be extremely helpful in finding bugs and suggesting fixes.

Data analysis is another area where AI offers significant benefits. Since DevOps focuses on rapid changes and subsequent testing, AI is ideally suited to analyze the data and recognize trends and issues. This can be especially useful in seeing patterns that can signify an issue with the code. Similarly, AI can be used to automate much of the testing process, speeding up what can be achieved with human testing alone.

AI is extremely useful in the realm of diagnosing, analyzing and triaging security vulnerabilities. Especially with the rapid pace of DevOps development, security testing and analysis must be equally fast and robust to prevent serious vulnerabilities from creeping in. AI is ideally suited to this application.

When taken together, the underlying picture becomes clear: AI excels at many of the things that define successful DevOps. DevOps is about streamlining communication, speeding up development, rapidly testing and identifying bugs, fixing those issues and then iterating as quickly as possible.

AI and machine learning (ML) are the perfect match for DevOps due to the speed with which they can analyze data. This helps teams identify problems, suggest solutions and facilitate faster communication between operations and development.

Just as important, AI can continue to perform these roles even after software is considered “complete” and operating live. In these instances, AI can continue to monitor the software in the background, see how it functions over time and suggest improvements.

Conclusion

It remains to be seen what role AI will play in society at large. Whether it will live up to the hopes of its supporters or deliver the doom of its critics is anyone’s guess.

In the meantime, however, it can be a vital tool in areas where its natural strengths are allowed to shine. The unique challenges and opportunities DevOps presents is just such an example.

Companies with established DevOps would do well to consider integrating AI and ML into their operations. Similarly, companies looking to implement DevOps will likely find the process much easier with the help of AI.

TagsAIDevOps
Matt Milano
Technical Writer
Matt is a tech journalist and writer with a background in web and software development.

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DevelopersAugust 21, 2020
How AI Is Impacting DevOps
AI is transforming DevOps, helping it achieve its full potential.

Artificial intelligence (AI) is one of the hottest topics in the tech industry. Depending on who you ask, AI is poised to either be one of the most important technological and societal advances in history, or one of the most dangerous pursuits to date.

Whatever AI’s role in the big picture, there is no doubt it is already transforming multiple industries and disciplines. One such example is DevOps.

What Is DevOps

As the name implies, DevOps is the intersection of Development and IT Operations. While it would seem, on the surface, that these two departments should work seamlessly together, the reality is often a far different story.

Developers and operations personnel often approach problems from two entirely different perspectives, which can sometimes lead to a breakdown in communication over the best way to approach things. Similarly, there are times when it’s desirable to have a measure of separation between development and operations, especially when it comes to some security issues.

DevOps is designed to help break down these barriers and help the two departments work together as efficiently as possible. DevOps is the natural progression of the Agile and Lean approaches. Lean focuses primarily on the bare minimum that’s required, what adds value, and ignores everything else. Agile, is designed to break down old methods and processes, especially those that are more about doing things a certain way than being productive.

The Agile Manifesto says it best:

We are uncovering better ways of developing

software by doing it and helping others do it.

Through this work we have come to value:

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiation

Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

In many ways, DevOps blends these two philosophies, focusing only on what adds value and removing any obstacles to that. Similarly, DevOps ignores established convention in favor of true productivity. To aid in these goals, rather than monolithic updates, DevOps emphasizes frequent, incremental improvements, backed up by robust testing.

How AI Is Helping DevOps

One of the biggest ways AI is helping DevOps is in the realm of communication. DevOps emphasizes frequent communication between the departments, but putting it into practice can be overwhelming, especially in larger organizations. AI chatbots can be a valuable tool to ease this process, facilitating communication and keeping up with the volume of information.

Another significant way AI is assisting DevOps is in the development process. AI has reached the point where it can streamline development, through code analysis and automatic completion. Similarly, AI has proven to be extremely helpful in finding bugs and suggesting fixes.

Data analysis is another area where AI offers significant benefits. Since DevOps focuses on rapid changes and subsequent testing, AI is ideally suited to analyze the data and recognize trends and issues. This can be especially useful in seeing patterns that can signify an issue with the code. Similarly, AI can be used to automate much of the testing process, speeding up what can be achieved with human testing alone.

AI is extremely useful in the realm of diagnosing, analyzing and triaging security vulnerabilities. Especially with the rapid pace of DevOps development, security testing and analysis must be equally fast and robust to prevent serious vulnerabilities from creeping in. AI is ideally suited to this application.

When taken together, the underlying picture becomes clear: AI excels at many of the things that define successful DevOps. DevOps is about streamlining communication, speeding up development, rapidly testing and identifying bugs, fixing those issues and then iterating as quickly as possible.

AI and machine learning (ML) are the perfect match for DevOps due to the speed with which they can analyze data. This helps teams identify problems, suggest solutions and facilitate faster communication between operations and development.

Just as important, AI can continue to perform these roles even after software is considered “complete” and operating live. In these instances, AI can continue to monitor the software in the background, see how it functions over time and suggest improvements.

Conclusion

It remains to be seen what role AI will play in society at large. Whether it will live up to the hopes of its supporters or deliver the doom of its critics is anyone’s guess.

In the meantime, however, it can be a vital tool in areas where its natural strengths are allowed to shine. The unique challenges and opportunities DevOps presents is just such an example.

Companies with established DevOps would do well to consider integrating AI and ML into their operations. Similarly, companies looking to implement DevOps will likely find the process much easier with the help of AI.

AI
DevOps
About the author
Matt Milano -Technical Writer
Matt is a tech journalist and writer with a background in web and software development.

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