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7 Ways Machine Learning Can Optimize DevOps

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23 Feb 2020CPOL5 min read 9.1K   2  
DevOps is a methodology that aims to create a synergy between software engineering and IT operations teams.
This article discusses seven ways in which machine learning can optimize devops. These include improving efficiency of performance feedback, helping with analyzing available data, making use of trends, correlating data across sets and platforms, managing alerts efficiently, optimizing metrics and goals, and testing software properly. Machine learning technology can enhance your practices, DevOps methodologies, and tools, and make sure that your processes are optimized as well as that the final result is superb.

By streamlining and optimizing processes and workflows and improving tools, this methodology is essential for the efficiency of the software delivery process and bringing value to businesses. This set of practices heavily relies on a tremendous amount of data for orchestrating and monitoring the processes, as well as identifying faults and bugs in a timely manner. While all this data is vital for DevOps, it’s also one of its downsides.

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Namely, server logs themselves can balloon up to a few hundred megabytes a week, while monitoring tools can amass as much as several gigabytes of data within a short timeframe.

No wonder it’s difficult for teams to interpret these huge data sets.

Enter machine learning and AI.

1. Improve the Efficiency of Performance Feedback

While the development performance heavily relies on using the best AI tools out there, there’s still one critical component – proper, timely, and transparent feedback.

One of the crucial aspects of DevOps is leveraging continuous feedback loops at every stage of the process, which also means monitoring currently running applications. In other words, an almost instantaneous, real-time insight is required for this to work.  

Thanks to Jira, Jenkins, Git, and similar products, the software delivery process is more transparent.

The thing is that monitoring all these applications and processes results in generating huge amounts of data – logs, performance metrics, etc. 

Implementing machine learning can do wonders as it is capable of identifying different irregularities and deviations in these massive data sets early on. In a nutshell, long build times, frequent task switching, inefficient use of resources, or large amounts of code will be spotted before they snowball into an actual issue and complicate the development process.

2. Help With Analyzing the Available Data

Similarly, the fact that there’s so much data also results in not analyzing all of it. Instead of that, thresholds are set that are used to trigger action.

Unfortunately, although this approach is practical in terms of saving time, it sends a great deal of immensely useful information to waste. By focusing on outliers, whose main function is to alert but not to inform, this tactic misses out on the wealth of data that can be analyzed and interpreted.

Machine learning applications can be trained to analyze all data segments and make conclusions that can be later used for predictive analysis.

3. Make Use of Trends 

As we have already mentioned, machine learning can be used to anticipate and forecast issues.

Namely, regular analytics focuses on faults and issues that have already occurred. This is a somewhat belated approach as we’re learning about a problem before we can do anything about it.

That’s why machine learning can be trained to observe, analyze, and identify patterns that happen below these thresholds and thus notice trends that happen over time. Such an output is much more actionable as it allows you to prevent issues before they take place and save a lot of time and resources.

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4. Correlate Data Across Sets and Platforms

Trends can be properly interpreted only if you look into multiple variables at a time since they result from the interactions of a couple of different measures that occur simultaneously.

For example, an increase in response time can take place only when there are fewer transactions at the same time.

As these trends can’t be observed and established with traditional analytics, machine learning can come in handy with its capacity to spot different correlations and interactions between different variables.

5. Manage Alerts Efficiently

As DevOps is all about efficiency and speed, its alert system is programmed to identify flaws and bugs quickly.

This means that the system generates an avalanche of alerts, all of which seem to be of the same level of priority and importance. Needless to say, teams can’t figure out how to properly react.

Machine learning can help teams prioritize their tasks by analyzing and assessing different factors such as previous behaviors, the source from which an alert comes, etc. So, instead of rushing to handle an alert that could wait, teams will be pointed in the right direction, and attend to the most urgent matters.

6. Optimize Metrics and Goals

 If there’s a specific metric or goal that you want to optimize, it’s best to leverage an adaptive machine learning system.

Simply put, adaptive machine learning systems are those that do not rely on a known result. They analyze, process, and optimize a particular characteristic of certain input data.

Amazon, for example, uses the so-called dynamic pricing model. In other words, online retail behemoth takes advantage of machine learning technology to analyze different factors such as demand for a particular item, a particular visitor’s intent, or other retailers’ pricing. It’s worth mentioning that Amazon changes its prices every 10 minutes in order to optimize them. The price of a product can be increased or slashed even after you put it in your shopping cart – if the adaptive algorithm assessed that you’re likely to buy, you might get a discount as an incentive.   

DevOps processes can benefit from adaptive machine learning in a similar manner.

The point is to enable the system to change its parameters during operation and tweak them in order to get the best result in a given situation. 

7. Test Software Properly

Besides being implemented in the development stages of a product, machine learning will also find its place in the field of software testing.

Numerous tests which are performed during the testing, quality assurance, and quality control stages of the process, which also means producing gargantuan amounts of data. Machine learning and AI algorithms can be applied to these test results in order to spot and identify some poor coding practices. As these algorithms are powerful and sophisticated, they can identify different error patterns and help development teams improve their practices and eliminate these errors in the future.

As you can see, although nothing can beat hard work and putting a lot of effort into your projects, machine learning technology can enhance your practices, DevOps methodologies, and tools, and make sure that your processes are optimized as well as that the final result is superb. 


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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