DevOps teams often collect huge volumes of data—but most organizations aren’t leveraging this data as well as they could be.

While many companies know they can harness data to power automation and optimize development, they often overlook that there’s no way DevOps and SRE teams can manually analyze all this data themselves. Without AI assistance, these companies are leaving valuable insights on the table.

Machine learning capabilities are essential to process growing data volumes, discover opportunities for automation, and move toward DevOps’ data-driven future. Organizations can only optimize integration, deployment, reliability, and efficiency for large codebases—and relieve excessive DevOps workloads—with the help of AI-powered automation.

There is so much data that DevOps engineers have access to—data from the CI, build process, production elements—that can be used to optimize the process. From creating code fixes and tests automatically, to selectively building and deploying code, there are many ways DevOps teams can benefit from machine learning and use data that they’re currenting discarding. This is the future, and this transformation is already happening. It all starts with collecting the data.

The capabilities of machine learning in DevOps are infinite, but most organizations have no idea where to start. Here are 10 ways you can use ML to transform how your DevOps team operates.

1. Improve Your Testing Environment

With a machine learning algorithm trained to understand what a successful release looks like, DevOps teams can automate multiple elements of a testing environment. That way, AI can speed up test times, identify novel errors, and even review test results to help developers deliver high-quality applications in less time.

Take Meta for example. Meta (formerly known as Facebook) began leveraging ML to improve regression testing for code changes as early as 2017. Meta’s solution goes beyond looking at code dependencies to anticipate issues; instead, the engineering team built a model based on features from previous code changes to predict which tests are relevant to new code and to evaluate whether the new code will pass these tests before it’s integrated into the shared testing environment.

With this solution, Meta automated and streamlined testing to prevent regressions from entering the trunk code, strengthening its testing environment and improving collaboration among developers.

2. Find Patterns and Trends Across Different Data Sources

Pulling seemingly unrelated data together from different sources takes a ton of time to do manually. Plus, it’s difficult to justify spending time collecting and analyzing data when teams don’t know what they’re looking for. But with ML tools, teams can combine huge data sets from various sources to discover unexpected patterns and detect trends.

For example, these models can instantly detect known issues and discover unknown issues in code integrations based on historic patterns. This can help teams predict faults before they’re pushed to production.

Machine learning can also reveal trends and correlations that human analysts would normally miss. By reviewing code changes and application performance within a wider historical context, teams can find trends and use those insights to drive DevOps decisions, optimize processes, and achieve better results.

3. Provide Intelligent Alerting

DevOps teams receive tons of alerts every day, but only some of those alerts genuinely require their immediate attention. These alerts pull DevOps teams away from the work at hand, leading to unexpected deployment delays. Plus, a barrage of alerts can cause fatigue, which means engineers may start tuning out alerts and miss an important warning.

One way machine learning can minimize alerts is by defining criteria to automatically group related alerts and deliver them at a designated time. That can help reduce alert blindness and give DevOps teams access to the information they need when they’re free to address it. Plus, that can ensure that when a known bad alert appears, DevOps can quickly reprioritize and redirect their attention to solving the problem.

Other ML models can reduce alerts by independently resolving known issues or directing tickets to the best-qualified person to manage it.

4. Enhance Security

Developers can’t always see the security gaps that accompany new integrations. As an ML model learns your team’s typical usage and access patterns, it can review logs and security data to identify when something abnormal happens that may not be observable to the human eye. That includes assessing each developer’s normal behavior to identify potential insider threats, which often go undetected in development and test environments.

An algorithm can also instantly trigger controls when it discovers a security incident, saving DevSecOps teams time on containing the incident and allowing them to focus on finding the gap that caused it.

5. Automate Access Control

Reducing privileged credential sprawl is one way DevOps teams can secure their development and test environments, reducing the likelihood of breaches. But often, lower access means DevOps spends more time manually provisioning and deprovisioning access when engineers and developers need it.

ML models can interpret security controls and predict when users may need access to different resources throughout their workflow. Then, it can automatically provision the right access to streamline workflows without compromising security and deprovision access when it’s no longer needed. When an abnormal access request comes through, ML tools can trigger an alert for DevOps teams to investigate and verify authorization.

6. Prevent Recurring Issues

Many teams train machine learning models by introducing known recurring issues. While quickly discovering these issues can be helpful, most DevOps teams would rather find ways to solve those known issues for good. However, it’s hard to prioritize finding the root cause of an issue when they’re focusing on maintaining uptime.

With ML tools working in the background to detect unseen patterns and trends, teams can gain new insight into what happens before a known issue occurs. Then, AI can triage issues automatically and help teams discover how to permanently remediate the issue.

7. Streamline Delivery

When data from any tool in your tech stack can be integrated into a machine learning algorithm, DevOps teams can collect development metrics and garner valuable insights to optimize application delivery.

Making integration and deployment more efficient starts with understanding the bottlenecks standing in the way of delivering new features and bug fixes quickly. For example, if developers have too many conflicting priorities, they may be switching tasks too frequently or cutting corners, ultimately slowing down production. DevOps can share these insights with managers to help them better support their teams and address concerns that would otherwise go unnoticed.

8. Model New Integrations on Known Good Releases

While DevOps teams typically leverage machine learning to identify and reduce known bad issues, ML can also be used to improve the quality of new releases by reviewing integrations against known good releases.

Each known release helps strengthen the ML model, teaching the algorithm what successful integrations look like for your team. Evaluating new integrations against a strong ML model allows DevOps teams to detect both known and unknown issues rapidly. Plus, it adds nuance and context to QA results, giving teams the insight they need to continuously improve future iterations and speed up deployment.

9. Recognize Issues in Production

Maintaining uptime is a fundamental priority for DevOps teams, and leveraging ML is a great way to identify abnormal behavior that may lead to a failure in production.

By detecting patterns with how users typically use the application, ML models can find early indicators of production issues and give DevOps teams a jumpstart on investigating the issue, dramatically speeding up mean time to detection. If that abnormal behavior poses a threat to the application, teams can also leverage machine learning to expedite debugging before an outage or failure occurs.

Even if resolving the root cause takes longer than expected, machine learning tools can help reduce the impact of an outage and automate remediation efforts, too.

10. Automate Infrastructure as Code (IaC)

Automated code completion is a well-known developer use case for machine learning, but most DevOps teams don’t consider how they can leverage these same capabilities to streamline managing and provisioning IaC.

Instead of manually provisioning infrastructure for testing, DevOps teams can leverage IaC to instantly deploy and configure the right infrastructure, ultimately allowing them to speed up testing and move to production faster. Machine learning can be leveraged to edit and distribute configuration files automatically with intelligent code completion, which makes managing your infrastructure even easier.

The Future of DevOps Is in the Data

There’s no denying that machine learning plays a vital role in the future of DevOps, and these use cases are just the beginning of the transformation occurring in the DevOps space. Now is the time to start introducing ML in your organization, but ML models are only as valuable as the data they’re built with.

Start now by preserving all the data you can, including build artifacts, test results, execution times, build/runtime logging, and even production traffic. The more data you have, the more valuable insights you can gain. Soon, these insights will present powerful opportunities for your DevOps team to optimize its processes, so you always stay one step ahead of the competition.

Want more insights into the future of DevOps? Follow Redefine to discover the latest innovations in the DevOps space.

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