Model Experiments Tracking and Registration Using MLflow on Databricks

Discover how to streamline model experiments tracking and registration using MLflow on Databricks in this 23-minute video. Learn to automate crucial tasks in the machine learning lifecycle, focusing on data acquisition, preparation, and experimentation. Explore the integration of StreamSets and MLflow to efficiently manage datasets, create models iteratively, and track various artifacts throughout the development process. Gain insights into setting up data sources, transformer pipelines, and automating pipeline jobs. Follow along with a comprehensive demo that covers live cluster setup, pipeline creation, code summaries, and status checks, ultimately enhancing collaboration between data scientists and data engineers in the MLOps ecosystem.

Introduction
Model Experiments
Data Sources
Transformer Pipeline
Demo Overview
Live Cluster
Pipelines
Column Names
Code Summary
Pipeline Run
Automate Pipeline Job
Check Pipeline Status