Databricks MLflow


                Databricks MLflow

Databricks MLflow is an open-source platform designed to manage the entire machine learning (ML) lifecycle, from experimentation and tracking to deployment and monitoring. It provides a centralized repository for ML models and associated artifacts, making collaborating, reproducing experiments, and managing model versions easier.

Key features and benefits of using MLflow on Databricks:

  • Experiment Tracking: Log parameters, metrics, and artifacts (e.g., models, plots) to keep track of ML experiments and compare results.
  • Model Registry: A centralized model store for managing model versions, transitions (staging, production, archived), and model lineage.
  • Deployment: Easily deploy models to various targets, such as REST endpoints, batch inference jobs, or streaming platforms.
  • Integration with Databricks: Seamless integration with Databricks notebooks, jobs, and data stores for a streamlined ML workflow.
  • Scalability: Leveraging Databricks’ distributed computing capabilities for training and deploying large-scale ML models.

How Databricks extends MLflow:

  • Managed MLflow: A fully managed version of MLflow on the Databricks platform, offering enhanced reliability, security, and scalability.
  • MLflow Model Registry in Unity Catalog: Centralized model governance, cross-workspace access, lineage, and deployment.
  • Enhanced UI and collaboration features: Streamlined UI for managing experiments, models, deployments, and team collaboration features.

Getting started with MLflow on Databricks:

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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