Model behavior: How Metaplane's ML model sees what others miss

At Metaplane, the company is taking a fundamentally different approach to building its data observability solution, one that's purpose-built for the unique patterns and challenges of data systems. Unlike many other data observability tools that rely on general-purpose time series models, Metaplane has built its own bespoke ML model designed specifically for the patterns it sees in data systems. This approach allows for fine-grained precision in detecting issues such as missing updates during expected update windows and increases that are smaller than historically observed patterns. The company's model is also distribution-aware, recognizing that data metrics often follow specific non-normal patterns. Additionally, Metaplane's model continuously updates itself after every new observation, giving it a big performance boost in terms of accuracy. This approach has resulted in a better experience for users, with smarter alerts, persistent visibility of issues, and customization without complexity. By leveraging its custom-built model, users can expect to reduce alert fatigue, focus on genuine issues, detect subtle issues that generic models would miss, and maintain data reliability.