Using Machine Learning to Improve Your DevOps Practice

Alice NjengaMon, 04/08/2019 - 08:34

The use of machine learning technology in businesses and industries continues to rise. Machine learning, which is a branch of artificial intelligence, revolves around the idea of a system that learns from data and can make decisions with minimal human intervention. It’s no wonder then that this technology would benefit the world of application development.

In this article, we’ll look at how you can use machine learning to improve your DevOps practice.

How Machine Learning Works

Before we see how machine learning fits into DevOps, let’s look briefly at how machine learning works. Machine learning isn’t new, but its increasing momentum has connections with recent developments. This mainly has to do with the rise in huge amounts of data that require processing to solve various problems. Machine learning is already showing up in different fields, such as computational finance for credit scoring, manufacturing for predictive maintenance, and many other areas.

Machine learning is applicable to complex tasks or problems that involve large amounts of data. This technology uses two types of techniques: supervised learning and unsupervised learning. Supervised learning predicts future output by training a model on known input and output data, whereas unsupervised learning looks for hidden patterns or intrinsic structures in input data.

Initially, machine learning was based on pattern recognition and the ability to perform specific tasks without being programmed. Of interest today is machine learning’s ability to apply mathematical calculations automatically over and over, faster and faster.

How Does Machine Learning Fit Into DevOps?

So what does machine learning have to do with DevOps?

The interest in machine learning is connected to the growing volumes and varieties of data. This data requires cheaper and more powerful computational processing and affordable data storage.

Similarly, in application development, we’re witnessing an increase in data sets used in the entire life cycle of an application. It’s difficult to process the massive amounts of data generated by the DevOps team. As a result, it’s important to have a layer that will harness such data to help DevOps achieve end-to-end automation. This presents a good opportunity to apply machine learning. With all this data to process, imagine what could happen if a DevOps team uses a shortcut. That would be the start of major problems because the developers won’t dive deeply into all the available data. As such, some crucial data may fail to be analyzed, which will eventually affect the overall outcome of an application.

Machine learning and DevOps share some commonalities—namely, enhanced automation, better collaboration, and efficiency.

Imagine having an easier way to deal with all the data generated in DevOps processes, such as server logs and transaction traces. With machine learning, such large-scale data is analyzed in real time.

A DevOps team that applies machine learning will be able to mine huge and complex data sets to reveal new insights and identify patterns more quickly. As a result, the team will be empowered to analyze data and gather predictive analytics. This will benefit the DevOps team in several ways: They’ll be able to quickly identify the root cause of a problem, deliver quality at higher speeds, analyze the business impact, and avoid failures in production.

Areas in DevOps That Will Benefit From Machine Learning

If there’s one thing that makes business executives happy, it’s the idea of cost saving, speed, and greater precision (reduced errors). So, let’s look at some areas of DevOps where we can apply machine learning.

  • Evaluation of past performance: This will be important during the application creation process because it’ll be easy to evaluate the success of past applications. The machine learning algorithms may even make suggestions to developers on the applications they’re building.
  • Performance feedback: Continuous feedback is crucial for DevOps at every stage of application development. Because machine learning can handle massive amounts of data, it enhances the feedback loop, which is critical for developers.
  • Managing alerts: Machine learning will help teams spot errors quickly. This is possible by prioritizing such errors so that the DevOps team will know the source and severity according to past behavior.
  • Software testing: Tests always result in large amounts of data. With machine learning algorithms, it’ll be easy to identify patterns that result in coding errors. Such information will help developers become more efficient.
  • Data comparison across platforms and tools: DevOps teams use multiple tools and platforms. However, using machine learning, the correlation of all this data gives the team a holistic view of an application.

Has This Been Done Before?

When new technologies are generating interest, people are always curious about how the technology works. You don’t want to implement new technology just because it sounds nice and then it ends up messing things up. Having said that, let’s see how machine learning is implemented in application development.

Well, actually, you may already be using machine learning. This is because more and more next-generation tools in the DevOps stack now support machine learning to some extent. Unfortunately, the overall impact is still limited. DevOps teams are too busy putting out fires. Again, the tools in the DevOps stack are mostly black boxes that operate as isolated data silos.

A good example of how future application development is about intelligent systems learning on their own is TensorFlow, a product of Google. An open source machine learning tool, TensorFlow helps integrate intelligent features in applications.

Challenges

Implementing new technologies always comes with challenges. When you’re considering using machine learning to improve DevOps, it’s crucial that you’re aware of what to expect.

As I mentioned earlier, the available DevOps tools use the black box approach. Because the approach conflicts with normal machine learning, the algorithms will require adjusting to fit business needs. What this means is that DevOps programmers must understand how the infrastructure works. Thus, they’ll need to acquire machine learning skills. Machine learning comprises applied mathematics (calculus, logarithms, regression analysis, etc.). This may be a bit of a challenge because most DevOps engineers aren’t mathematicians.

With such a machine learning gap, an organization will have to hire new people or have the existing team learn new skills. Putting together another new team is a daunting task for organizations. It also isn’t easy to manage such a complex project within a set time frame and budget.

What the Future Holds

A while back, using machine learning in application development would have been unthinkable. Yet the advances in these technologies promise to make processes easier and faster. Therefore, despite the challenges, the adoption of machine learning will continue to grow with time.

The reason for the growth is the expectation that, in the future, machine learning systems will require less data for their training and, as a result, the systems will learn more quickly with smaller data sets.

I know it’s all exciting. But before adopting machine learning in any process, it’s important to remember that the system will be as effective as the training it receives. Adopting machine learning isn’t enough. If you fail to pair your algorithms with the right tools and processes, you won’t get the most value out of machine learning.

Conclusion

The success of DevOps depends on automation to help reduce the time and effort of the involved software development processes.

As we’ve seen, machine learning technologies can help improve DevOps, and the impact on the business is growth. This is possible with enhanced management of the huge data sets that the DevOps team is continually producing.

A machine learning-driven DevOps infrastructure offers remarkable benefits across an entire enterprise. This is why managers are already taking steps to boost their teams through hiring and training.