Introduction to Databricks Machine Learning

Summary

Learn how to use Databricks Machine Learning.

Description

Description

Databricks Machine Learning offers data scientists and other machine learning practitioners a platform for completing and managing the end-to-end machine learning lifecycle. This course guides practitioners through a basic machine learning workflow using Databricks Machine Learning. Along the way, students will learn how each of Databricks Machine Learning’s features better enable data scientists and machine learning engineers to complete their work effectively and efficiently.

Learning objectives

  • Describe a basic overview of Databricks Machine Learning.

  • Create a feature table for downstream modeling using Feature Store.

  • Automatically develop a baseline model using AutoML.

  • Manage the model lifecycle using Model Registry.

  • Perform batch inference using the registered model and feature table.

  • Schedule a monthly model refresh using Databricks Jobs and AutoML.

Prerequisites

  • Beginning-level knowledge of the Databricks Lakehouse platform

  • Intermediate-level knowledge of Python

  • Intermediate-level knowledge of machine learning workflows

Learning path

This course is part of the Data Scientist and Machine Learning Engineer learning paths. 

Proof of completion

Upon 80% completion of this course, you will receive a proof of completion.