Machine Learning Model Development

This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. Participants will explore key ML techniques, including regression and clustering, while leveraging Databricks’ powerful capabilities. The course covers MLflow integration for model tracking, Databricks Feature Store for feature management, and Optuna for hyperparameter tuning. Additionally, participants will learn how to accelerate model training with Databricks AutoML. By the end of the course, learners will have real-world, practical skills to develop, optimize, and deploy machine learning models efficiently in the Databricks environment.

Note:

This is the second course in the 'Machine Learning with Databricks’ series.
Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Skill Level

Associate

Duration

Prerequisites

At a minimum, you should be familiar with the following before attempting to take this content:

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)

• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (databricks-sdk, REST endpoints)

• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model versioning

• Understanding of machine learning fundamentals, including model training, evaluation, batch inference, and real-time deployment concepts

• Intermediate experience with Unity Catalog for data governance and model registry management

• Basic familiarity with Feature Engineering concepts, including feature tables, feature lookups, and offline vs online feature stores

• Understanding of Delta Lake operations (create tables, perform updates, optimize files, and liquid clustering) and data storage optimization techniques

• Basic knowledge of Apache Spark and PySpark for distributed data processing and User Defined Functions (UDFs)

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Customer registration Partner registration

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

Purchase now

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Analyst

SQL Analytics on Databricks

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights. You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL. Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Languages Available: English | 日本語 | Português BR | 한국어

Paid & Subscription

Lab

Associate

Data Warehousing Practitioner

SQL Programming and Procedural Logic

In this course, you will explore core SQL programming and procedural logic skills within Databricks, focusing on how to build modular, maintainable, and production-ready analytics workflows. You’ll start by learning about key SQL constructs—such as Common Table Expressions (including advanced recursive CTEs), session-scoped objects like temporary views and tables, and User Defined Functions—that make your SQL development cleaner and more reusable. The course then guides you through advanced SQL scripting techniques using compound statements, variables, control flow, conditionals, loops, and robust error handling to create structured and flexible SQL processes. You will also learn how to guarantee data consistency across these multi-step pipelines using ACID-compliant multi-statement transactions. Finally, you’ll discover how to encapsulate logic into SQL Stored Procedures and orchestrate complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform.

Paid & Subscription

Lab

Associate

Platform Administrator

Get Started with Data Governance on Databricks

In this course, you will explore how Unity Catalog enables secure, centralized data governance and fine-grained access control on Databricks. You will learn about table and volume types, catalog and schema configuration, group-based access management, and strategies for migrating existing access controls into Unity Catalog. The course also explains how to design and apply fine-grained controls such as row-level security, column masking, and attribute-based access control, how to combine these mechanisms across data and AI assets, and how to align them with broader governance requirements for compliant, scalable access management.

Languages Available: English | 日本語 | 한국어

Paid & Subscription

Lab

Onboarding