Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

MLflow is the most widely adopted vendor-neutral open-source MLOps tool in enterprise settings, yet running a local mlruns folder is worlds apart from operating a shared, multi-user MLflow service. This course provides a deep, hands-on dive into the entire MLflow ecosystem.

Separating the backend store (PostgreSQL) from the artifact store (S3/MinIO), custom and automatic logging across scikit-learn/PyTorch/XGBoost, bundling preprocessing and business logic into a single mlflow.pyfunc package, automated quality gates with mlflow.evaluate and GenAI/LLM Tracing are all practised. Participants leave with a production-ready multi-user MLflow architecture and a standard internal SDK usage guide.

Who Should Attend?

  • MLOps engineers deploying open-source experiment and model management on-premises or in the cloud
  • Data scientists who need to package and hand off models in a standardised format
  • Infrastructure and platform engineers operating internal data science platforms
  • Prerequisite: Python proficiency and working experience with common ML libraries

Key Takeaways

  • Deploy a scalable, authenticated MLflow Tracking Server backed by PostgreSQL and S3/MinIO
  • Record nested runs, custom diagnostic plots and dataset lineage using the MLflow Tracking API
  • Wrap preprocessing, model weights and post-processing into a single portable artefact with mlflow.pyfunc and ModelSignature
  • Gate candidate models against baseline thresholds automatically using mlflow.evaluate
  • Use MLflow Tracing and Evaluate to monitor both classical ML and LLM/RAG applications on one platform

Curriculum

MLflow Architecture and Enterprise Server Setup
  • Tracking Server, Backend Store (PostgreSQL) and Artifact Store (S3/MinIO/Azure Blob) topologies
  • Proxied artifact access, authentication and team-level permission controls
MLflow Tracking API and Advanced Logging Patterns
  • Limits of autologging, custom metric/parameter logging, system metrics and dataset lineage with mlflow.data
  • Organising cross-validation and hyperparameter sweeps with parent/child (nested) runs
MLflow Models, Flavours and Custom PyFunc Packaging
  • The MLmodel manifest, ModelSignature, input_example and locking conda/uv/pip environments
  • Bundling feature transforms, ensembles and business logic into one artefact with mlflow.pyfunc
MLflow Evaluate and Model Registry Lifecycle
  • Automated metric computation, custom evaluators and baseline validation thresholds with mlflow.evaluate
  • Model versioning, Registered Model Aliases (@champion, @challenger) and deployment webhooks
GenAI / LLM Observability (MLflow Tracing) and Serving
  • Step-by-step latency, token and prompt tracing across LLM and RAG chains
  • Building inference containers with mlflow models build-docker for REST and batch scoring

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