Get Started with Databricks for Generative AI

This course offers a practical introduction to the Databricks Data Intelligence Platform, focusing on its key components and features for building and deploying generative AI systems. Participants will learn how Databricks facilitates the development of scalable generative AI solutions and explore tools such as AI Search, the Agent Framework, and MLflow's generative AI capabilities for model tracking and logging. This course includes hands-on experience in constructing and evaluating Retrieval-Augmented Generation (RAG) pipelines, deploying generative AI agents, and leveraging evaluation frameworks to optimize performance. By the end of the course, learners will be equipped with the skills to design, deploy, and monitor common generative AI applications on the Databricks Data Intelligence Platform.

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

Skill Level

Onboarding

Duration

Prerequisites

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

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

• Basic knowledge of Python programming and working with APIs (Databricks SDK, external model integrations)

• Understanding of machine learning fundamentals, including model training, evaluation, and deployment concepts

• Basic familiarity with generative AI concepts (large language models, prompt engineering, hallucinations, retrieval-augmented generation)

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

• Basic knowledge of vector search and similarity search concepts for document retrieval

• Familiarity with MLflow for experiment tracking, model logging, and evaluation frameworks

• Understanding of Delta Lake and data management concepts (tables, schemas, data formats)

Self-Paced

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

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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

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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

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

Get Started with Databricks for Data Engineering - Mandarin Chinese

本课程介绍在 Databricks Data Intelligence Platform 上执行基础数据工程工作流所需的核心技能。您将探索工作区、使用 Unity Catalog，并学习数据工程师在 Databricks 上日常使用的基础构建模块。

本课程采用实操方式。您首先熟悉工作区，然后针对每个主题完成配套的演示（Demo）和实验（Lab）笔记本。演示会由讲师或引导式笔记本带您了解某个概念；实验则要求您自行应用刚刚所学的内容。

课程内容涵盖：

• Databricks Data Intelligence Platform，以及 Databricks 工作区、Unity Catalog 和笔记本如何协同运作。

• 创建和管理 Delta Lake 表。

• 使用 INSERT、UPDATE 和 DELETE 修改数据。

• 探索 Delta 表的版本历史记录和时间旅行。

• 使用 Lakeflow Connect 选项（CTAS、上传 UI 和 COPY INTO）引入数据。

• 构建奖牌架构管道，通过 Bronze、Silver 和 Gold 层转换数据。

• 使用 Lakeflow Jobs 实现管道自动化。

• （附加内容）使用 Spark Declarative Pipelines 构建声明式管道。

Free

Onboarding

Machine Learning Practitioner

Get Started with Databricks for Machine Learning

In this course, you will develop the foundational skills needed to use the Databricks Data Intelligence Platform for executing machine learning workflows and supporting data science workloads. You will explore the platform from the perspective of a machine learning practitioner, covering topics such as building and managing features with Feature Engineering in Unity Catalog, end-to-end model lifecycle management with MLflow, and pipeline orchestration with Lakeflow Jobs. Additionally, you will learn about real-time model inference with Databricks Model Serving and experience Databricks' transparent, conversational approach to model development through Genie Code - Data Science Agent Mode, where you use natural language prompts to generate, run, and iteratively refine executable ML workflows directly in your notebook. The course includes instructor-led demonstrations, culminating in a comprehensive lab that reinforces the concepts covered throughout.

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

Free

Onboarding