MLOps Best Practices 2026

Automated excerpt

Data Governance and Continuous Validation\n\nIn the current MLOps paradigm, data is not just an input; it is a first-class citizen. Continuous data validation is the practice of automatically checking data quality before it reaches your training pipeline or serves a live inference request. \n\nArchitecture Description:\nA robust data governance architecture features a schema validation layer sitting between the data lake and the training pipeline. In production, models are subject to data drift (changes in the input data distribution) and concept drift (changes in the relationship between input and output).

Selected automatically from source text; not independently written or fact-checked. Read the original for full context.