From the course: MLOps and Data Pipeline Orchestration for AI Systems
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Introducing MLflow
From the course: MLOps and Data Pipeline Orchestration for AI Systems
Introducing MLflow
- [Instructor] In the hands-on demo that's coming up next, we're going to be working with MLflow. MLflow plays a key role in MLOps by enabling experiment tracking, model versioning, and lifecycle management to ensure reproducibility, traceability, and seamless deployment of machine learning models. MLflow is an open-source platform designed to manage the complete machine learning lifecycle. MLflow provides tools for tracking experiments, packaging code into reproducible runs, managing and deploying models, and offering a central model registry for collaboration and governance. Essentially, MLflow aims to address the challenges of reproducibility, deployment, and collaboration in the ML workflow. Here are the basic components that make up the MLflow platform. Now, there's a lot here on screen. Let's break this down bit by bit. MLflow Tracking logs experiments with parameters, metrics, and artifacts. You can use experiment tracking to log and compare code versions, input data…
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