Understanding MLOps vs AIOps

While AIOps and MLOps have concepts and tools that may overlap, they are quite different topics, and serve different purposes in the ITOps ecosystem. A quick search on Google for “MLOps vs AIOps” will show you that many people wonder, and try to understand, the difference between the two.

First, Machine Learning Operations (MLOPs)  defines the process of operationalizing Machine Learning, and applying ML to different systems and  workflows. The focus is on the process, and Machine Learning tools are used in the implementation. MLOps seeks to extend efficiency across the business organization, but it does not specify tools, but rather the category of tools to use in implementation.

In contrast, AIOps involves automating the lifecycle of IT Operations. AIOps is a specific type of machine learning that is applied to an IT organization. AIOps applies different AI tools, such as real time data streaming, cross-stream data correlation, automated root cause analysis, and incident prediction and prevention. The complexity and scale of ITOperations has exploded over the last 20 years – overwhelming businesses with the volume and breadth of data it has to manage. Although this data can be extremely valuable to an organization, it needs the proper AI tools to understand it and provide actionable insights to feed back into the system.

Each part of the AIOps process involves important things like anomaly detection, incident prediction, root cause analysis, and self-healing. The power of AIOps focuses on accuracy and automation. The ideal outcome for AIOps is to focus on the mean time to resolve a problem, but the mean time to discover, predict, and prevent a problem before it impacts the business. AIOps leverages the power of observability tools and recommends next steps to solve the problem.

Both MLOps and AIOps are important parts of adapting to the complexities of modern computing. DevOps and SRE teams own both processes. AIOps answers the question “how can I reduce downtime and optimize performance of my apps and service,  while MLOps answers the question “how can I ensure my ML models are supporting my apps and services as expected. By implementing MLOps, businesses can effectively integrate new technologies in their work processes. If businesses apply the right AIOps technology, they can leverage the AI innovation in prediction and prevention that will give them the competitive edge in the market.