Databricks Machine Learning Fundamentals

Overview

In this course, you will learn the fundamentals of using Databricks for machine learning. You will tackle the challenge of disjointed tools and master production-grade machine learning on Databricks. This course guides you through the complete end-to-end ML lifecycle on a single platform, giving you the practical skills to build robust, deployable solutions. You'll start by building a solid data foundation, using Apache Spark to ingest, clean, and engineer high-quality features. Next, master MLOps by using MLflow to systematically track and compare experiments, bringing reproducibility and rigor to your workflow to identify the best model. Finally, close the loop by deploying your models into production. You will use the MLflow Model Registry for versioning and governance before deploying your model as a live, real-time REST API endpoint. Through a series of hands-on labs and a final capstone project, you'll gain the confidence to build, track, and deploy sophisticated ML models, leaving with a portfolio-ready project that makes you a more effective and valuable data professional. This course is designed for intermediate learners who are familiar with basic machine learning concepts and want to learn how to apply them in Databricks for real-world projects. Learners should have a basic understanding of Python, including Pandas and Scikit-learn, along with fundamental machine learning concepts. By the end of this course, learners will be able to apply the full ML lifecycle on the Databricks platform, from data preparation and analysis to model deployment. They will also gain the skills to track experiments and manage models using Databricks and MLflow, ensuring a streamlined, reproducible workflow. Additionally, learners will be equipped to deploy machine learning models effectively using the MLflow Model Registry and Databricks Model Serving.

Syllabus

Getting Started with Databricks for ML

This module introduces the core concepts of the Databricks Machine Learning platform. Learners will get a hands-on tour of the workspace, explore how to ingest and prepare data, and perform initial exploratory analysis to set the foundation for the ML lifecycle.

Building and Tracking Models with MLflow

This module dives into the core of MLOps on Databricks. Learners will discover how to use the integrated MLflow platform to track experiments, log models, and compare results to ensure reproducibility and select the best-performing model.

Model Deployment and Management

This final module closes the loop on the ML life cycle. Learners will take their best model from the previous module and use the MLflow Model Registry to version, manage, and deploy it for real-time inference.