8 ways Artificial Intelligence can improve DevOps

Source – hub.packtpub.com

DevOps combines development and operations in an agile manner. ITOps refers to network infrastructure, computer operations, and device management. AIOps is artificial intelligence applied to ITOps, a term coined by Gartner. Makes us wonder what AI applied to DevOps would look like.

Currently, there are some problem areas in DevOps that mainly revolve around data. Namely, accessing the large pool of data, taking actions on it, managing alerts etc. Moreover, there are errors caused by human intervention. AI works heavily with data and can help improve DevOps in numerous ways. Before we get into how AI can improve DevOps, let’s take a look at some of the problem areas in DevOps today.

The trouble with DevOps

Human errors: When testing or deployment is performed manually and there is an error, it is hard to repeat and fix. Many a time, the software development is outsourced in companies. In such cases, there is lack of coordination between the dev and ops teams.

Environment inconsistency: Software functionality breaks when the code moves to different environments as each environment has different configurations. Teams can run around wasting a lot of time due to bugs when the software works fine on one environment but not on the other.

Change management: Many companies have change management processes well in place, but they are outdated for DevOps. The time taken for reviews, passing a new module etc is manual and proves to be a bottleneck. Changes happen frequently in DevOps and the functioning suffers due to old processes.

Monitoring: Monitoring is key to ensure smooth functioning in Agile. Many companies do not have the expertise to monitor the pipeline and infrastructure. Moreover monitoring only the infrastructure is not enough, there also needs to be monitoring of application performance, solutions need to be logged and analytics need to be tracked.

Now let’s take a look at 8 ways AI can improve DevOps given the above context.

1. Better data access

One of the most critical issues faced by DevOps teams is the lack of unregulated access to data. There is also a large amount of data, while the teams rarely view all of the data and focus on the outliers. The outliers only work as an indicator but do not give robust information. Artificial intelligence can compile and organize data from multiple sources for repeated use. Organized data is much easier to access and understand than heaps of raw data. This will help in predictive analysis and eventually a better decision making process. This is very important and enables many other ways listed below.

2. Superior implementation efficiency

Artificially intelligent systems can work with minimal or no human intervention. Currently, a rules-based environment managed by humans is followed in DevOps teams. AI can transform this into self governed systems to greatly improve operational efficiency. There are limitations to the volume and complexity of analysis a human can perform. Given the large volumes of data to be analyzed and processed, AI systems being good at it, can set optimal rules to maximize operational efficiencies.

3. Root cause analysis

Conducting root cause analysis is very important to fix an issue permanently. Not getting to the root cause allows for the cause to persist and affect other areas further down the line. Often, engineers don’t investigate failures in depth and are more focused on getting the release out. This is not surprising given the limited amount of time they have to work with. If fixing a superficial area gets things working, the root cause is not found. AI can take all data into account and see patterns between activity and cause to find the root cause of failure.

4 Automation

Complete automation is a problem in DevOps, many tasks in DevOps are routine and need to be done by humans. An AI model can automate these repeatable tasks and speed up the process significantly. A well-trained model increases the scope of complexity of the tasks that can be automated by machines. AI can help achieve least human intervention so that developers can focus on more complex interactive problems. Complete automation also allows the errors to be reproduced and fixed promptly.

5 Reduce Operational Complexity

AI can be used to simplify operations by providing a unified view. An engineer can view all the alerts and relevant data produced by the tools in a single place. This improves the current scenario where engineers have to switch between different tools to manually analyze and correlate data. Alert prioritization, root cause analysis, evaluating unusual behavior are complex time consuming tasks that depend on data. An organized singular view can greatly benefit in looking up data when required.

6 Predicting failures

A critical failure in a particular tool/area in DevOps can cripple the process and delay cycles. With enough data, machine learning models can predict when an error can occur. This goes beyond simple predictions. If an occurred fault is known to produce certain readings, AI can read patterns and predict the signs failure. AI can see indicators that humans may not be able to. As such early failure prediction and notification enable the team to fix it before it can affect the software development life cycle (SDLC).

7 Optimizing a specific metric

AI can work towards solutions where the uptime is maximized. An adaptive machine learning system can learn how the system works and improve it. Improving could mean tweaking a specific metric in the workflow for optimized performance. Configurations can be changed by AI for optimal performance as required during different production phases. Real-time analysis plays a big part in this.

8 Managing Alerts

DevOps systems can be flooded with alerts which are hard for humans to read and act upon. AI can analyze these alerts in real-time and categorize them. Assigning priority to alerts helps teams towards work on fixing them rather than going through a long list of alerts. The alerts can simply be tagged by a common ID for specific areas or AI can be trained for classifying good and bad alerts. Prioritizing alerts in such a way that flaws are shown first to be fixed will help smooth functioning.

Conclusion

As we saw, most of these areas depend heavily on data. So getting the system right to enhance data accessibility is the first step to take. Predictions work better when data is organized, performing root cause analysis is also easier. Automation can repeat mundane tasks and allow engineers to focus on more interactive problems that machines cannot handle. With machine learning, the overall operation efficiency, simplicity, and speed can be improved for smooth functioning of DevOps teams.

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