Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
Data observability is continuous monitoring of data pipelines and datasets so that problems are caught before users notice. It watches freshness, volume, schema, distribution and lineage.
Observability is the ability to understand what a running system is doing, and why, from the data it emits. That data is mainly logs, metrics and traces, which let engineers answer new questions without shipping new code.
An evaluation set is a fixed collection of inputs with expected outcomes, used to score an AI system the same way every time it changes. It plays the role a regression test suite plays for ordinary code.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
DevOps automates building, testing and running code, while MLOps extends that practice to the data, training and evaluation that machine learning models need. MLOps also tracks model drift after release, a problem ordinary application code does not face.
Predictive analytics uses historical data and statistical or machine learning models to estimate what is likely to happen next. Common targets are customer churn, product demand and credit risk.
A clear proven process, that delivers
You've seen our ideas - now go deeper.
Discover more insights, guides, and engineering practice.