AI Agent Fundamentals

This foundational course introduces AI agents and their use in enterprise applications on Databricks, including the Mosaic AI platform and Agent Bricks. Learners will examine what AI agents are, how they function, and how they mimic human reasoning to handle complex tasks.

The course covers real-world agent use cases and provides a basic introduction to advanced topics such as agentic workflows and multi-agent systems. It also explores how Agent Bricks simplifies the development of enterprise-ready agents across various applications, with demos showing how to build and use agents on Databricks.

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

Skill Level

Introductory

Duration

1h 30m

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

• Completed the Get Started with Databricks for Machine Learning (Onboarding) course or possess equivalent foundational experience with the Databricks platform.

- Learners should be familiar with basic Databricks workspace operations, including navigating the UI, creating notebooks, and accessing common workspace features.

• Basic understanding of artificial intelligence and machine learning fundamentals.

- This includes introductory knowledge of large language models (LLMs), their capabilities, and common use cases.

• Beginner-level familiarity with prompt engineering and natural language interaction with AI models.

- Learners should have experience writing simple prompts and understanding how AI models respond to natural language inputs.

• Introductory knowledge of Unity Catalog concepts for governance and asset management.

- Learners should be aware of how data and AI assets are organized, secured, and governed within the Databricks platform.

• Basic understanding of document processing and information extraction concepts.

- This includes familiarity with different file formats, data types, and how unstructured data can be processed for downstream AI applications.

• Familiarity with no-code and low-code development approaches.

- Learners should understand when no-code or low-code tools are appropriate for rapidly building and prototyping AI solutions.

• Introductory awareness of AI agent concepts and workflows.

- Learners should understand the distinction between structured and unstructured data processing tasks and how AI agents can orchestrate multi-step workflows.

Self-Paced

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Upcoming Public Classes

Data Engineer

Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect, a scalable and simplified solution for ingesting data into Databricks from a wide range of sources. You’ll begin by exploring the different types of Lakeflow Connect connectors (Standard and Managed) and learn various data ingestion techniques, including batch, incremental batch, and streaming ingestion. You'll also review the key benefits of using Delta table and the Medallion architecture

Next, you’ll develop practical skills for ingesting data from cloud object storage using Lakeflow Connect Standard Connectors. This includes working with methods such as CREATE TABLE AS SELECT (CTAS), COPY INTO, and Auto Loader, with an emphasis on the benefits and considerations of each approach. You’ll also learn how to append metadata columns to your bronze-level tables during ingestion into the Databricks Data Intelligence Platform. The course then covers how to handle records that don’t match your table schema using the rescued data column, along with strategies for managing and analyzing this data. You’ll also explore techniques for ingesting and flattening semi-structured JSON data.

Following this, you’ll explore how to perform enterprise-grade data ingestion using Lakeflow Connect Managed Connectors to bring in data from databases and Software-as-a-Service (SaaS) applications. The course also introduces Partner Connect as an option for integrating partner tools into your ingestion workloads.

Finally, the course wraps up with alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with a strong foundation to support modern data engineering use cases.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Free

Associate

Generative AI Engineer

Generative AI Application Deployment and Monitoring

After consuming the content inside of this learning pathway, you should be able to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring generative AI applications. This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Note:

1. This is the fourth course in the 'Generative AI Engineering with Databricks’ series.

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!

Free

Associate

Answering Business Questions with Databricks Genie

This course introduces Databricks Genie, a conversational analytics tool that lets you ask plain-language questions of your organization's data and get governed, secure answers. You'll learn what a Genie Space is, how it keeps your data secure, and best practices for getting the best results. The course closes with a hands-on game where you will compete to investigate a business mystery.

Free

Onboarding