AIOps empowers your DevOps organization to build and grow.

The way we build, test, launch, and update products is quickly evolving. In a world of constant innovation and updates, the work of building and maintaining a product is never completely done. As technology changes, so too does the scope and nature of those performing the development and operations roles.

When the focus is to optimize for the best user experience, there are often changes to fix and make the product better. The creator of a product is best equipped to review, fix, and improve the product they created.

The DevOps engineer combines the role of creator and maintainer. Patrick Debois initially introduced this concept about a decade ago, when teams recognized it was not efficient for R&D teams and IT Operations teams to exist in silos. Shortly thereafter, “you write it, you own it” became a popularized term.

How does this new role make sense when the responsibilities of development and maintenance are combined? The only way for the DevOps to succeed is to find ways to lift some of the previous burdensome tasks allocated to each role.

Enter AIOps. The right AIOps platform will give DevOps teams a holistic view across the products they build and run.  Previous generations of ITOps teams were required to manually comb through different data sets to find, troubleshoot, and fix a problem. AIOps relieves this burden by automatically monitoring data streams across different tools and sources. The best AIOps systems will automatically detect anomalies, find root causes and provide actionable insights to resolve the problem. With AIOps, many problems can be detected before an error occurs, thus minimizing the impact on business.

Automation in application development, testing, and running of products allows Developers to focus on the mission critical decisions. DevOps represents the best of both worlds: creators who are the most familiar with their products are the ones responsible for solving them. However, they can only effectively do so with tools that can help deliver intelligent insights across all different systems.

Other Resources

A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.