AI and DevOps: how artificial intelligence is affecting DevOps culture

AI and DevOps? Artificial intelligence (AI) is a technological advancement that’s beginning to change numerous  industries. Though AI isn’t a new idea, it has been gaining a lot of media attention in the past few years.

This attention is mostly due to the predicted impact of AI on education, healthcare, transportation, and even the legal field. However, it’s widely believed that the most solid and game-changing influence will be on DevOps.

AI is especially suited for a culture with DevOps, as the latter is mostly focused on task automation. It also monitors the delivery process of software, thus making sure that everything is thoroughly complete on time. DevOps doesn’t exactly eliminate the human factor, but it encourages repeatable processes that can reduce errors and enhance efficiency.

Wondering how exactly AI may affect DevOps culture?

Let’s explore a few ways in which AI creates an impact on the DevOps culture.

It contributes to giving valuable feedback

Among the key aspects of DevOps is the way it needs continuous feedback throughout the process. To this end, it uses monitoring tools for feedback on all application performances. Here, artificial intelligence is making its mark already as it uses big data, such as log files and performance metrics.

With all these technological concepts working together, it becomes easier for DevOps to identify problems early on. This also means that you can follow the right recommendations. These suggestions then pass on to the DevOps teams so that they can retain the application service. By enhancing the feedback loops, AI tends to bring improvement in the DevOps processes.

It enables easier communication

When organisations want to switch over and start using DevOps methodology, they might face several challenges. Among the most common obstacles are those of feedback and communication between teams. While human interaction may be an important part of all this, the sheer amount of information within a company’s system requires them to install a range of channels.

These channels then set up and revise the various workflows as quickly as possible. With the automation technology that DevOps offers, AI systems such as chatbots are of the utmost importance. Chatbots have certainly come a long way, even replacing human customer services in many areas. AI contributes here in the way that the communication channels for DevOps become more proactive and streamlined.

It helps in achieving data correlation

In order to give the maximum amount of benefit, DevOps has to simplify its tasks. This makes them repeatable and results in quality control as well. As the competitive market and the target audiences are getting more complicated, it might be more difficult to break down tasks properly.

With AI, however, one can absorb data streams while monitoring the health and performance of a certain process. By analysing the data you have, you may use AI to find valuable correlations across several platforms and link them with the necessary monitoring tools.

As a result, every DevOps team can get a clear and holistic analysis of how their application is performing. Even if you have little knowledge about understanding the performance impact of various frameworks within your organisation, some insights for Scala performance by AppOptics will help you take the next step.

It facilitates an alerts system

If there must be a failure on the part of its teams, DevOps encourages them to find the fault as quickly as possible. Therefore, it’s essential to have quick alerts for any flaws. While the quick alerts might be useful, there’s usually a barrage of them at any one point.

Most of them have the same high level of urgency, hence making it difficult for a DevOps team to respond properly.

With AI, DevOps teams can prioritise the alerts and deal with them according to past information. These include the general behaviour, magnitudes of the current situation, and the source of the alert. When data saturate the system, the machines help to sort it out.

It helps evaluate the past

AI may also help out DevOps developers in the creation of the applications. They do this by looking at past applications and how they were successful in their building and compiling processes. They also provide information about the successful testing and operational performances. With the algorithms, DevOps teams may also get recommendations on their code writing and applications for achieving a higher level of effectiveness.

It has an impact on software testing

Finally, we might be able to see even more uses for AI with regards to DevOps in the future. These include the software testing aspect, which has functional, user acceptance, regression, and unit tests. By applying AI algorithms, the DevOps developers will identify patterns in the test results, hence pointing out errors due to poor coding.

With this information, the DevOps teams may work better in order to produce error-free results. By using historical data as well, implementing AI may prove to be valuable for fine-tuning deployment strategies when it’s time to move applications from the testing environment to the production line.

Wrapping up

When artificial intelligence is taken forward, it has the potential to process a huge amount of information to make menial tasks easier. This frees up humans trained in IT and other skills to pursue more specific targets. With artificial intelligence learning patterns, anticipating problems, and presenting solutions, operations and development would become unified.

As a result, we may hope to see industries running more smoothly with DevOps in the future.

Ashley Lipman, award-winning writer, OutReachMama