Learn essential techniques for managing datasets and tracking machine learning experiments. This course provides practical guidance on implementing data versioning with tools like DVC and systematically logging experiment parameters, metrics, and artifacts using MLflow. Build reproducible and manageable ML workflows.
Prerequisites Python & ML Basics
Level:
Understand Core Concepts
Grasp the importance and principles of versioning data and tracking experiments in the ML lifecycle.
Implement Data Versioning
Utilize Data Version Control (DVC) to manage datasets, track changes, and ensure data reproducibility.
Implement Experiment Tracking
Employ MLflow Tracking to log parameters, metrics, code versions, and artifacts for ML experiments.
Integrate Tools
Combine data versioning and experiment tracking into a cohesive MLOps workflow.
Build Reproducible Pipelines
Structure ML projects for better reproducibility, collaboration, and debugging.
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