MLOps

MLOps (Machine Learning Operations) is the practice of applying DevOps principles to machine learning systems, focusing on automating and streamlining the deployment, monitoring, and maintenance of ML models in production. It bridges the gap between data science and IT operations to ensure reliable, scalable, and reproducible ML workflows.

Companies need MLOps now because the shift from experimental ML to production-grade systems requires robust pipelines for continuous integration, delivery, and monitoring of models. With AI regulations tightening and model drift becoming a critical issue, businesses like Dataiku, Databricks, and Stripe prioritize MLOps to maintain compliance, reduce downtime, and scale AI solutions efficiently.