Building Reliable Conversational Agents with Genie

This course teaches you how to design, build, and maintain a Databricks Genie Agent, a natural language interface that enables business users to ask questions about governed data and receive SQL-backed answers without writing code.

You will learn how Genie Agents fit into the Databricks AI/BI product family and how they translate natural language into reliable SQL queries. The course focuses on what it takes to create a Genie Agent that delivers accurate, consistent, and trustworthy results.

You will follow a complete end-to-end workflow, from understanding source data and defining benchmarks to configuring and refining a Genie Agent using the full set of Knowledge Store curation tools. These include metadata, synonyms, prompt matching, SQL logic, example queries, and text instructions.

You will also learn how to share Genie Agents with business users through Genie One, understand how Unity Catalog governance is automatically enforced, and use monitoring and user feedback to continuously improve quality over time.

By the end of the course, you will be able to create and manage a production-ready Genie Agent that delivers governed, self-service conversational analytics at scale.

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.

Skill Level

Onboarding

Duration

Prerequisites

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

• General familiarity with the Databricks workspace - You can navigate the workspace UI, attach compute resources, and run notebooks.

• Familiarity with Unity Catalog concepts - You understand catalogs, schemas, and tables in Unity Catalog. Prior experience with permissions, tags, or column masks is helpful but not required.

• Intermediate SQL proficiency - You can read and write SQL queries including SELECT, JOIN, GROUP BY, and aggregate functions.

• Access to a Databricks workspace with the Databricks SQL entitlement and CAN USE permission on a serverless SQL warehouse.

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

Model Development at Scale

In this course, you will learn to design and implement machine learning workflows that scale from a single machine to distributed compute on Databricks. The curriculum is organized into three modules: foundations of model development (when and why to distribute, and how Spark executes work), scalable model development with Spark ML and MLflow, and distributed model training with Ray and PyTorch — closing with a framework for choosing the right tool for a given workload.

Through hands-on demos and labs, you will build distributed Spark ML pipelines using Transformers, Estimators, and the fit/transform paradigm; scale existing pandas code with the Pandas API on Spark; and track, compare, and govern experiments with MLflow 3 and Unity Catalog. You will then train models across a cluster with Ray Train and PyTorch (TorchDistributor and DeepSpeed), and learn to match each workload to the right framework based on data size, model size, hardware, and cost.

The course concludes with a capstone that takes a problem end to end — engineering features, training a model at scale, tracking it with MLflow, and registering it in Unity Catalog with a governance alias. (Production deployment, serving, and monitoring are covered in the separate Advanced MLOps course.)

By the end, you will be able to choose, build, and justify distributed, reproducible, and governed model-development workflows on Databricks.

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

Professional

Data Engineer

Get Started with Data Governance on Databricks - Mandarin Chinese

在本课程中，您将通过动手演示和综合实验室，浏览 Unity Catalog 并了解 Databricks 上的细粒度访问控制。您将学习表类型、目录和架构配置、基于组的访问管理以及访问控制迁移策略。本课程包含以下演示：应用基于行级安全和列掩码的细粒度访问控制、基于属性的访问控制、组合控制、迁移控制，以及一个用于全面治理实施的实验室。

Languages Available: English | 日本語 | 한국어 | Português BR | Français | Español | 普通话

Free

Onboarding