Get Started with Databricks for Machine Learning

In this course, you will explore the core features and functionalities of Databricks Express Setup, a streamlined way to get started with Databricks. The course is designed to help you quickly set up and navigate a serverless workspace while providing a comprehensive understanding of the credit-based trial system, including the $400 allowance. Divided into six modules, it starts with an introduction to Databricks Express Setup, followed by an exploration of its key features and benefits. You will learn to create and manage serverless workspaces, perform exploratory data analysis using Unity Catalog, and gain insights into collaboration through data sharing.

Additionally, you’ll be guided through trial management and upgrade options, ensuring you can effectively transition from trial to paid accounts. The course also includes an internal-only module on the evolution of trial credits and account activation methods. By the end of the course, you will have a solid foundation in using Databricks Express Setup for data and AI workloads, enabling you to confidently explore and analyze data, collaborate with peers, and manage your Databricks environment efficiently.

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!

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.

Note: 

1. This course is the second in the series of Advanced Machine Learning. 

2. 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!

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