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…

Background The COVID pandemic forced many education programs to rely upon online learning as their primary means of education for the academic year. Tools that had been used to supplement a classroom-first industry were now the main portal for students and educators. …

If you search for the answer to “What is AIOps,” you’ll find a fairly standard definition. However, reviewing the results of that search showcases a problem: it’s difficult to understand the difference between Observability and AIOps. Because there are some similarities in the value use cases, such as — “receiving…

It was one week before Kazzle was launching its new product. The company had dominant market share in the astronomy community and was known worldwide for its optical filters for telescopes. With Kazzle, views of space could be augmented with virtual worlds developed on a SaaS platform. …

Scenario A global query service from a web search company experienced a major service outage. The service consists of 13 different replication zones all over the world for reliability. The service outage was detected only after all 13 zones experienced unavailability. The Software reliability engineer (SRE) had no clue where the…

Scenario A major customer-facing service from a cloud monitoring company was down for three days. Customers complained that they could not load the dashboard. The whole engineering team was pulled into the debugging process. The developers believe the service outage was caused by data corruption in their Cassandra data store. The…

Capabilities Every vendor’s AI seems the same. What is the best approach for using AI to detect anomalies? Unsupervised machine learning: Look for unsupervised machine learning versus supervised machine learning to avoid hand-labeling machine data. Hand-labeling data is not scalable. Multiple data inputs: Look for anomaly detection across multiple types of machine data — specifically, logs and metrics plus change events. Detecting the right anomalies requires analyzing data…