Using Machine Learning to Improve the Performance of DevOps

Developers need to understand the intersectionality between DevOps technology and machine learning. Machine learning algorithms can significantly improve the effectiveness of DevOps applications.

Incorporating Machine Learning Into Your DevOps Model

It is important to be aware of the different ways that machine learning can be applied to DevOps. Before you begin implementing DevOps practices, it is important to carefully define your objectives and recognize the biggest shortcoming of traditional DevOps environments.

DevOps processes are invaluable for generating massive data sets for different applications. They can be useful for streamlining various methodologies and handling troubleshooting queries.

Unfortunately, data scalability is a double-edged sword for countless DevOps developers. As they accelerate their data production, they might be overly inundated with data. Sorting through mountains of data to find meaningful information can be virtually impossible. Developers are forced to scan data quickly and make time-pressured decisions based on information that stands out. This leads to two different heuristics that can erode the value of their DevOps Data:

• They search for data sets that they expect to be relevant to the application at hand. This often causes them to cave to a certain type of confirmation bias. They have already made up their minds to what type of data is most relevant to their DevOps environment. As a result, the data itself becomes meaningless.
• Even while searching for relevant data, they might find more than they can process. Certain data sets might be more likely to stand out to them. As a result, they might overlook highly valuable entries that are relevant to the application.

Experienced developers appreciate that data scalability necessitates better data processing solutions for DevOps projects. Machine learning is proving to be one of the best solutions. Here are some of the core benefits.

Making Decisions Based on a Granular Analysis Rather Than the Visibility of Data Thresholds

DevOps teams simply can’t handle quantitative analytics on their own as data scalability exceeds their neural limitations. They must adapt by focusing on data thresholds instead. The problem is that there are countless data sets between these thresholds that provide treasure troves of insights that should not be ignored.

Machine learning is able to seamlessly analyze these vast data sets and make more nuanced deductions. They can account for millions of data points that would be overlooked by human DevOps decision makers.

Observe Trends Between Different Data Sets

Modern DevOp environments are capable of handling numerous arrays or vectors of data. When using sophisticated object-oriented programming algorithms in conjunction with Hadoop tools, DevOp teams can theoretically find complex relationships between almost any data sets. The data sets don’t need to be logically connected to make these types of inferences. This is something that a company developing a mattress reviews app needs to consider. It could use a DevOp environment to look for complex relationships between mattress features and customer sleep patterns. However, they would need to use a well optimized machine learning environment to handle this analysis.

The problem is that developers need to go out of their way to find these correlations. They are usually looking at data sets in isolation. They rarely consider that certain correlations may even exist, so they often go unnoticed when DevOps developers are trying to handle analytics manually.

Machine learning is able to recognize trends across data sets that would otherwise be overlooked.

Develop a Stronger Understanding of Our Historical Shortcomings

Mistakes are a reality that even the most experienced DevOps teams have to contend with. The problem is that it is difficult to grasp the contextual meaning behind those mistakes.

When most DevOps developers document their mistakes, they usually make one-time entries with little information for them to learn from. It is difficult to evaluate patterns of mistakes to improve their long-term efficiency and minimize their error rates.

This is another important feature of machine learning. It continually identifies mistakes made by the DevOps team and helps improve their production models to keep them from reoccurring.

Utilize Machine Learning With Your DevOps Projects

Machine learning is a gamechanger for most developers. They are finding it to be especially useful for DevOps projects.

What do you think?