The story of Artificial Intelligence (AI) and Machine Learning (ML) is all about hope and hype. And that’s hardly surprising.

On the one hand, there’s a technology that promises to revolutionize fields as diverse as agriculture, manufacturing, education, and healthcare. On the other, there’s so much media attention that it gets impossible to cut through the hype and, proverbially speaking, separate the wheat from the chaff.

And though making heads or tails of it all is difficult, DevOps is for sure poised to capitalize on the opportunities that AI and ML offer, such as automation of tasks, data analysis, and improvement of efficiency.

Think about it this way:

DevOps generates tons of data. The more data you collect, the more complex it becomes to interpret it to streamline workflows, improve orchestration, detect faults, and more. In the meantime, machine learning allows processing massive datasets in real time to find patterns, generate predictions, and even suggest solutions to recurring problems.

ML use in devops

Given that, how is machine learning used in DevOps? And does it make sense to apply machine learning for DevOps in the first place?

Realistically: ML-driven DevOps can change your organization in many ways, from teasing out valuable insights from data to reducing manual work, with the proviso that your IT-organization is ML-ready; that is, it has a strong DevOps infrastructure with a sophisticated data and machine learning foundation on top.

In this post, I’m going to answer the questions above in more detail. I’ll also share just a few ways ML can make a difference for your DevOps.

Four Ways Machine Learning Can Improve DevOps

#1 Finding Trends Rather than Faults in Data

Data analysis in DevOps can be tricky.

For one thing, DevOps generates too much data. Then, that data is produced constantly in real time. And finally, engineers are human, and they should know what they’re looking for before they start to analyze anything.

continuous monitoring in devops

And that’s exactly why DevOps, though they’re equipped with a plethora of monitoring tools, scrutinize data only for specific faults, which they’re alerted about. Basically, DevOps fix errors but don’t look into the vast majority of data that may include clues as to why those errors have occurred in the first place.

Not very efficient, hah?

At the same time, AI and machine learning thrive when dealing with massive datasets.

ML-driven DevOps pipeline

When traditional monitoring and analytics fail, data can be fed to ML algorithms that are capable of finding the relationship between multiple variables, to find and predict trends rather than looking at sporadically occurring results of these trends.

And provided DevOps know all they should know about the trends that cause errors, they can enhance the entire workflow, thereby reducing manual work, increasing efficiency, and improving the system’s uptime.

#2 Driving “Start Fast, Fail Fast, Recover Fast” Culture

Applying ML to DevOps also makes sense in organizations that are over-capacitated by too many alerts and, basically, that are struggling to implement “start fast, fail fast, recover fast” culture in their IT.

Just imagine a situation:

Alert systems spot and notify DevOps about system failures, errors, and flaws — that’s their job. But what if your DevOps receive too many alerts all labeled with the same severity? They’ll eventually fix them all, yet they may start with less critical errors, unintentionally causing workflow and system inefficiencies.

Or, let’s put it this way: Alert systems may discourage employees, leading to human error and costly oversight, just because people can deal with only one dashboard at a time.

As you might have guessed, the sheer volume of data is the culprit, once again.

Machine learning provides the way to shift the burden of too much data — in this case, too many alerts — from human employees to the ML-driven prioritization system.

The system feeds on failure alerts data, analyzing past behavior of human employees, alert severity, alert source, and more. Once the system is trained, it can prioritize alerts by multiple factors, instantly delivering the most critical ones to DevOps.

The question is, what do alerts have to do with DevOps culture and DevOps success, then?

The answer is simple:

Organizations that react to important alerts in an efficient manner can deploy code more frequently and have a much faster lead time from commit to deploy. Their change failure rate is lower while their time to recover from incidents is lightning fast. Organizations as such start fast, fail fast, and recover fast, which gives them a competitive edge.

According to the 2018 State of DevOps Report by Puppet and Splunk, the top performers in DevOps:

    • Deliver 46 times more code deployments
    • Have 2,555 times faster commit to deploy lead times
    • Experience a 7x lower change failure rate
    • Ensure 2,604 times faster incident recovery times

DevOps success for top performers

In other words, a little thing like dealing with alerts in a smart way using machine learning can make a huge difference, driving the right culture and ensuring the efficiency of your IT-organization.

#3 Enabling Continuous Feedback Loops

Continuous feedback loops are key to DevOps success in many ways, from ensuring operational performance of both your applications and your teams to acting proactively on user feedback to drive UX improvements and customer engagement.

Actually, these three words — continuous, feedback, and loops — are very much synonymous with DevOps, as shown below:

Continuous feedback loops in DevOps

So, how can machine learning make continuous feedback loops, which are initially designed as streamlined and automated entities, even more efficient?

Well, any type of feedback is data, and data is what artificial intelligence and machine learning are good at.

For instance, imagine how much data (i.e. logs, performance metrics, etc.) your monitoring tools collect. Humans cannot realistically look at every log and every metric — in most cases, we react only if something goes wrong.

By contrast, machine learning algorithms can process and analyze any amount of data to identify problems and come up with recommendations in advance, which allows human workers to maintain and support the system.

In this manner, ML can empower DevOps through feedback analysis and enrichment along the entire DevOps lifecycle.

Feedback lifecycle in ML-driven DevOps

#4 Empowering QA and Developers

ML-driven DevOps is the answer not only to a wide range of problems that DevOps professionals have to solve but also to issues that developers and software testers experience all along the SDLC.

Any kind of testing — be it unit, regression, functional, and user acceptance tests — generates large amounts of data. And huge datasets always unlock the potential for machine learning. In this case, specifically, ML can be used to find the patterns of “buggy” coding, helping the dev teams to improve their coding practices.

Applying machine learning also makes sense to analyze development metrics. For instance, ML can identify correlations between delivery velocity and the number of bugs found.

In the future, AI and ML can be utilized to analyze entire applications, figuring out what has helped these apps succeed or fail code-wise. Insights as such will ensure that developers build the highest-quality applications from the start.

Conclusion

Data provides the bedrock for any machine learning task. (Check out this AI 101 to learn why.)

Fortunately, when it comes to DevOps, there’s no shortage of data generated by monitoring, continuous integration, continuous deployment, and other tools. On top of this, there are metrics, from velocity and defects found to lead time and deployment frequency.

Provided all of that data is fed to advanced ML algorithms, the system should learn to identify patterns, anticipate errors, and suggest solutions. The key here is to choose the right architecture and the right algorithms, to accurately train the system by pushing correctly labeled data into it, and to maintain the system’s performance (i.e. high prediction accuracy) on new data.

And all of that isn’t easy to do.

If you’re looking to empower DevOps in your organization through industry best artificial intelligence and machine learning practices, Squadex consultancy is your destination. We’re certified professionals in three critical areas — DevOps, Machine Learning, and Data Analytics — and are fully equipped to assist your business in adopting ML-driven DevOps.