MLOps

MLOps is the set of practices that unites data science, software engineering, and DevOps to ship machine-learning models from notebook to production—reliably and at scale. It covers the entire lifecycle: data versioning, feature engineering, reproducible training pipelines, model registry, automated testing, continuous integration/continuous deployment (CI/CD), and real-time monitoring for drift, latency, and bias. Tools such as MLflow, Kubeflow, and Tecton orchestrate experiments and track artifacts, while Kubernetes and Terraform provision repeatable, cloud-agnostic infrastructure. GitOps workflows trigger retraining when new data lands, and canary releases safeguard rollouts by routing a fraction of traffic to fresh models before full promotion. Key metrics include training time, inference throughput, and business KPIs like conversion rate. By enforcing governance, lineage, and automated rollback, MLOps turns experimental models into maintainable, auditable services that keep learning as data evolves.