Management of DevOps environments and processes may be time-consuming, especially with huge amounts of data. Artificial intelligence and machine learning can reshape the DevOps reality, eliminating many issues based on human limits. How are they connected and how AI can help DevOps teams? Find out in this article.
What is Artificial Intelligence?
In very simple words - Artificial intelligence (AI) is a phrase for systems and machines that copy human way of thinking and intelligence to perform tasks which usually require human involvement. It is also described as a field mixing computer science with datasets to allow solving problems and making decisions.
Connection of AI/ML and DevOps
AI based solutions and technologies support DevOps teams on every stage of software development life cycle. They help to analyze and use bigger volumes of data and as a result make all DevOps processes (testing, coding, deployment, monitoring) faster and more efficient. Artificial intelligence also boosts automation, which provides better addressing and resolving of issues both in data and applications.
Let’s see how AI can assist DevOps and different levels of their work
To keep applications healthy and improve them, continuous planning is necessary. In this process various inputs (issue tickets, requests, surveys, customer feedback etc.) are delivered constantly to make reports for the backlog. Here Natural Language Processing (NLP) helps with interpretation of feedback given in emails, voice messages, online comments or phone calls. The gathered data may be formed into a short summary and sent directly to the backlog, without humans’ intervention.
- Continuous integration
The process of CI means gathering parts of one code created by various developers and connecting them into one coherent whole. Here Natural Language Generator (NLG) can help by sending personalized alerts via a chatbot in case of any errors or faults. What’s more, historical data can be analyzed to discover patterns and most problematic points to avoid errors in future.
- Continuous testing
Artificial intelligence can take over the process of testing and reporting, but also help with quality assurance. A good example of this will be classification of errors and discovering duplicates during testing processes. Same as in two first levels, historical data and failed logs can be analyzed to find repeating patterns. Natural Language Processing can turn all of these findings into scripts useful in frameworks like Selenium or Appium.
- Continuous deployment
In many companies the deployment process is multileveled and controlled by automation triggered with only one click. However, there are still errors which lead to delays in software development and as consequence losing incomes or unsatisfied customers. Again the team can use AI, with historical data and specific logs generated during past deployments, to predict patterns, point faulty elements and possible future mistakes. These can be compiled with accurate results to find new solutions and make the deployment more predictable.
In the process of monitoring a lot of data is generated - via logs, events, metrics, alerts etc. AI- driven solutions involved in this process may use this data and help with identifying patterns and drawing conclusions. They can also discover suspicious or incorrect behavior, which may prevent breakdowns.
Thanks to AI-driven services, the costs of monitoring are reduced. Mostly because these costs are specific, based on service, team or region. In this case errors and issues can be solved faster. AI also works perfectly with one cloud-based metric in a multi-cloud environment. It learns the regular behavior and prepares cost forecasts, which later helps with budget planning.
Now, when we know the role and advantage of artificial intelligence in specific levels of DevOps teams work, we can focus on advantages in areas which are the results of these levels improved by AI.
Instead of long, riskful manual processes, DevOps can rely on AI data discovery and mapping to automate the process of transforming data into more useful shapes, the whole process gets easier and faster this way. They can also use predictive analytics and abilities to improve the process step by step. The data can be later processed and aggregated into meaningful metrics and trends, which will improve users’ work outcomes and decision making. Thus the manual intervention won’t be so often necessary and developers responsible for that may focus on more strategic tasks.
AI and ML systems have more precise features, which leads to higher level of security and efficiency. The centralized logging architecture allows users tracking and identifying suspicious activities which might be hacker attacks.
When companies have two teams - developers and operations - keeping balance between them might be challenging. Using AI and ML can help them work simultaneously and give one homogeneous view on the developed ecosystem and whole business.
Faster and more efficient processes which are usually time consuming, means more free resources for other activities. Companies with AI-driven solutions may focus on growth, offering new products/services and higher quality of existing ones, which later results in a bigger amount of new and satisfied clients.
Of course, implementing artificial intelligence in a company is a challenging process. Although AI and ML are growing and gathering more and more popularity, AI based tools are still less established than non-AI tools. This can concern team leaders and investors. Secondly, users may have different software and hardware requirements and synchronizing them is a time consuming process. And probably most important, the system must be trained with correct data, otherwise the results may be false.
Despite all these challenges, AI and ML is the future not only for DevOps, but the whole IT world. The growing list of Artificial Intelligence usability brings many processes to the completely new level and helps companies generate higher revenues in a shorter amount of time.