MLOps & Model Deployment
Ship, monitor and maintain machine learning and LLM systems in production.
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Most models never reach production, and many that do quietly degrade. MLOps courses close that gap. Topics include experiment tracking and model registries with MLflow or Weights & Biases, reproducible training pipelines, containerisation with Docker and orchestration with Kubernetes, CI/CD adapted for machine learning, feature stores, and serving patterns for batch and real-time inference. Monitoring gets serious treatment, covering data and concept drift, performance decay, and retraining triggers. LLMOps material extends this to generative systems with prompt versioning, evaluation harnesses and cost observability.