Applying machine learning to DevOps

Source – jaxenter.com

DevOps methodologies are rapidly increasing and generating vast and diverse data sets across the life cycle of entire application including development, deployment, and performance management. Only a robust analysis and monitoring layer can particularly harness this data for the ultimate DevOps goal that is end-to-end automation.

The rise of machine learning and its related capabilities, such as artificial intelligence and predictive analytics, has pushed organizations to explore implementing new analysis models that mainly rely on mathematical algorithms. The overall impact of these tools on data-driven automation is still limited due to busy teams of DevOps and a lack of practitioners who genuinely understand machine learning, AI, and predictive analysis.

The black box approach runs counter to conventional machine learning procedures and enables the analyst to adjust the algorithm iteratively until it becomes sufficiently accurate. Today, it is essential for DevOps engineers to know about the working of infrastructure, how to utilize DBaaS, and how to code in the cloud. Since most DevOps engineers are not mathematicians, adding machine learning algorithms to this skill set is not an easy thing.

Applying machine learning in DevOps

Despite the obstacles and challenges, the adoption of machine learning is only going to grow as high salaries push a number of IT engineers into this space. Although several DevOps vendors have added machine learning to their products, this does not exempt enterprises from the need to write their code in order to optimize their automation capabilities.

Many logs take up gigabytes of storage per week when there is too much data to manage. Most of the data generated in DevOps processes is on application deployment, server logs, and transaction traces results in application monitoring. The perfect way to analyze this large scale of data in real-time is to use machine learning. Let’s have a look at how machine learning enhances the practices of DevOps.

Look beyond the threshold

The DevOps teams analyze the entire data set as there is a plethora of data. They set thresholds for this reason as a condition for action. They primarily concentrate on outliners instead of focusing on substantial data chunks. Here, the problem exists as outliners usually provide indications but they don’t paint the detailed picture.

Learn from the history of data

DevOps teams do, occasionally, make mistakes. The professional originations of DevOps cannot resolve the problems encountered while they are in action. Machine learning systems can help them analyze the data and show what happened in recent time. It can verify from daily trends to monthly trends and provide a bird’s eye view of the application at any point in time.

Monitoring tools

Professional DevOps teams use more than one tool to view and act upon given data. Each specific device has its application monitoring ways on distinct grounds considering parameters like the health and performance of the application. These machine learning systems are capable of collecting inputs from all these tools and paint an integrated view.

Measuring orchestration

If your requirement is to measure orchestration process adequately, then you can use machine learning to determine the team performance. Limitations may result due to reduced orchestration. Therefore, looking at these characteristics can help you with both tools and processes.

Looking for faults

It deals with patterns of the investigation. Developers need to be proactive about looking for faults. If you have realized that these systems deliver specific readings in the failure event, a machine learning application can search for the particular patterns of a specific kind of fault. In that case, if you comprehend the underlying cause of the failure, you can find a way to prevent it from happening.

Drilling down to the root cause

Providing groups with a chance to set right performance or availability issues bodes well for the quality of the application. Most often, teams don’t research failures completely since they center on getting back online as soon as possible. In case, a robot gets them running fine; the cause mostly gets lost. Be careful not to let this slide.

Conclusion

Without the advent of big data, AI and machine models would just remain as models and would have never been implemented. IoT and cloud computing have an inter-reliant relationship. Likewise, the real-time effectiveness of machine learning systems relies on the DevOps processes that provide agile software development. Hence, applying machine learning to DevOps enhances their capability to perform cloud-based operations more efficiently.