The Shift from DevOps to Intelligent DevOps
Artificial Intelligence is set to transform DevOps automation as we know it. From facilitating faster troubleshooting to driving superior data management and improved overall efficiency, AI will be the antidote to the many complexities of the DevOps universe. Not only with this change how the DevOps ecosystem functions, it will also transform the way teams collaborate and execute.
But what will the DevOps landscape of the future look like? Artificial Intelligence and Machine Learning will drive improved efficiency and reduced complexities. With emerging technologies such as autonomics and analytics, DevOps of the future will be move from mere automation towards autonomous problem solving and predictive intelligence.
Let’s have a look at this emerging intelligent DevOps landscape and its progression from the traditional model to the advanced, predictive model. Here are the 3 types of intelligent automation systems in DevOps.
1. Automation that Obeys
These are basic automation systems that operate on pre-defined rules, trends, and patterns.
This includes basic DevOps automation systems (e.g. for continuous integration and testing, continuous deployment) and Pipeline lifecycle automation (e.g. traditional release management automation).
2. Automation That Analyzes
These systems employ algorithms and intelligence to extract meaning from data, execute judgment-based activities using diagnostic analytics, and make recommendations based on patterns.
This includes advanced automation systems such as – automated generation of test scenarios based on analytics on production logs, automated verification of system requirements based on customer experience analytics, virtual service that can auto-update based on change in application endpoints, etc.
3. Automation that Learns and Evolves
These systems understand context and adapt based on situations; they also employ predictive analytics to solve problems autonomously and intelligently.
This includes advanced automation systems such as – adaptive continuous delivery pipeline (e.g. learning systems that analyze past data to manage the pipeline based on dynamic rules)
and DevOps process optimization based on insight across the lifecycle (e.g. correlation of production log data with past code change data to determine the level of failure risk in different application modules)
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