Building Single-Agent Applications on Databricks

This course provides hands-on training for building single-agent applications on the Databricks Data Intelligence Platform. Students will learn to create AI agents that leverage Unity Catalog functions as tools, implement comprehensive tracing and monitoring with MLflow, and deploy agents using both traditional frameworks like LangChain and modern solutions like Agent Bricks. The course covers the complete agent lifecycle from initial tool creation and testing in AI Playground through production deployment with governance, evaluation, and continuous improvement capabilities.

Note: This is the second course in the 'Generative AI Engineering with Databricks’ series. It was previously named 'Generative AI Application Development'.

Skill Level

Associate

Duration

Prerequisites

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

1. Python-Specific Prerequisites

Learners must be comfortable writing production-quality Python, not scripts.

• Core Python syntax and data structures

• Functions, classes, and basic OOP patterns

• Exception handling and error propagation

• Decorators

• Type hints and docstrings

2. SQL-Specific Prerequisites

Learners must be able to define reusable SQL logic, not just query tables.

• Writing SELECT queries with filters and aggregations

• Understanding SQL data types and NULL handling

• Creating parameterized SQL functions

• Using CREATE OR REPLACE FUNCTION syntax

• Writing clear SQL comments for documentation

3. Databricks-Specific Prerequisites

Learners must be comfortable operating inside the Databricks platform.

• Navigating the Databricks workspace and notebooks

• Running notebook cells and interpreting outputs

• Understanding basic compute concepts (especially serverless)

• Using Catalog Explorer to inspect registered assets

• Awareness of Databricks-managed services (Model Serving, AI Playground)

4. GenAI / Agent-Specific Prerequisites

Learners must understand how LLM-powered agents behave, even if frameworks are taught in-course.

• What Large Language Models are and what they can and cannot do

• Basic prompt engineering concepts

• High-level understanding of Retrieval-Augmented Generation (RAG)

• Conceptual understanding of agent reasoning and tool invocation

• Familiarity with REST APIs and JSON payloads

5. Optional but Helpful (Not Required)

• MLflow fundamentals (tracking, model registry, tracing)

• Agent frameworks (e.g., LangChain)

6. Databricks-related recommended training: AI Agents Fundamentals, Get Started with AI Agents

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Upcoming Public Classes

Data Engineer

DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with Spark Declarative Pipeline and Jobs to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Declarative Automation Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.

Note:

1. This is the fourth course in the 'Data 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!

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

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Lab

Associate

Machine Learning Practitioner

Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.

Note: This course is the first in the series of Advanced Machine Learning.

Paid & Subscription

Lab

Professional

Apache Spark Developer

Introduction to Apache Spark™

This course offers essential knowledge of Apache Spark™, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.

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 | 한국어

Paid & Subscription

Lab

Associate