MLflow

MLflow is used as the system of record for the machine learning lifecycle, providing a consistent way to track experiments, manage models, and control promotion from experimentation to production.

It acts as a governance and reproducibility layer within the ML platform, sitting between training workflows and deployment systems.

Key Capabilities

• Experiment Tracking
  Captures parameters, metrics, and artifacts to enable structured comparison and reproducibility.

• Reproducible Projects
  Defines standardized execution environments and entry points for ML code.

• Model Packaging & Registry
  Provides a consistent model format and centralized registry for versioning and lifecycle management.

• Promotion & Stage Management
  Supports controlled transitions between development, staging, and production.

• Framework-Agnostic Design
  Works across multiple ML frameworks without locking workflows to a single vendor.

Experience & Platform Contribution

Implemented MLflow as a core MLOps component, enabling teams to manage experimentation and model lifecycle in a consistent, auditable manner.

Key contributions included:

• Establishing standard experiment logging patterns across training workflows
• Defining model registration and promotion workflows aligned with platform governance
• Integrating MLflow with orchestration, serving, and monitoring systems
• Enabling traceability between datasets, training runs, and deployed models
• Advising teams on reproducibility, model versioning, and lifecycle discipline

MLflow provided the backbone for moving models from research to production with confidence, ensuring visibility, control, and repeatability across the ML platform.