Agentic Application Fundamentals

AI has changed what developers are asked to build: agents, copilots and intelligent tools are moving from experiments into production, and the work is shifting from writing code to orchestrating models, data, and services. In this course, you'll learn how building agentic applications where the data lives removes the infrastructure overhead of stitching together fragmented stacks. Through a guided account-briefing demo, you'll see how Databricks Apps, Lakebase, and Agent Bricks (Genie Spaces, Knowledge Assistants and Supervisor Agents) work together on one governed, serverless foundation to deliver a production-ready agentic application.

Skill Level

Introductory

Duration

Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:

• Completion of Databricks Fundamentals, or equivalent familiarity with the Databricks Data Intelligence Platform.

• General awareness of generative AI concepts such as LLMs, agents and tool calling. No coding is required to follow the course.

• Optional, for following along hands-on: access to a Databricks workspace (or Free Edition) and basic familiarity with the workspace UI.

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

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

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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

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This course provides a comprehensive, hands-on overview of Databricks' modern approach to data warehousing, highlighting how a data lakehouse architecture combines the strengths of traditional data warehouses with the flexibility and scalability of the cloud. Working end-to-end with a fictional retailer (Vintage Audio Co.), you will explore the workspace, query governed Unity Catalog tables, keep Delta tables fast for analytical reads, load data incrementally with Auto Loader, model a star schema, and deliver consistent insights through Unity Catalog metric views — all from a single, governed environment, with AI-driven features (Predictive Optimization, Genie Code) built in throughout.

Free

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Data Architect

Get Started with Lakehouse Architecture on Databricks - Mandarin Chinese

在本课程中，您将从平台架构的角度探索 Databricks Data Intelligence Platform，重点关注以湖仓架构为基础的平台底座。您将了解基于湖仓的平台的范围、愿景和能力，考察 Databricks 如何与主流云提供商集成，并通过完善架构湖仓框架了解成功实施湖仓的关键要素。本课程还将介绍关键的架构原则、最佳实践和数据架构策略，帮助您使用 Databricks 加速组织的数据与 AI 计划。

Free

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Agent Evaluation on Databricks

This course teaches students how to systematically evaluate AI agents using MLflow's evaluation framework, addressing the unique challenges of non-deterministic AI systems that traditional software testing cannot handle. Students learn to implement various evaluation approaches including built-in judges for common criteria like correctness and safety, guideline judges for business-specific requirements, and custom judges for specialized needs. The course covers both offline evaluation using curated datasets and online production monitoring, with hands-on experience using MLflow's tracing capabilities to understand agent execution patterns and collect human feedback from different stakeholder types. Through practical demonstrations and labs, students develop skills in creating evaluation workflows that drive continuous quality improvements throughout the AI agent development lifecycle.

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