Generative AI Application Deployment and Monitoring

Ready to learn how to deploy, operationalize, and monitor 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.

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

Skill Level

Associate

Duration

Prerequisites

Familiarity with natural language processing concepts
Familiarity with prompt engineering/prompt engineering best practices
Familiarity with the Databricks Data Intelligence Platform
Familiarity with RAG (preparing data, building a RAG architecture, concepts like embedding, vectors, vector databases, etc.)
Experience with building LLM applications using multi-stage reasoning LLM chains and agents
Familarity with Databricks Data Intelligence Platform tools for evaluation and governance.

Outline

Model Deployment Fundamentals

Model Management
Deployment Methods

Batch Deployment

Introduction to Batch Deployment
Batch Inference
Batch Inference Workflows using SLM

Real-Time Deployment

Introduction to Real-Time Deployment
Databricks Model Serving
Serving External Models with Model Serving
Deploying an LLM Chain to Databricks Model Serving
Custom Model Deployment and A/B Testing

AI System Monitoring

AI Application Monitoring
Online Monitoring an LLM RAG Chain

LLMOps Concepts

MLOps Primer
LLMOps vs MLOps

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	May 12	11 AM - 03 PM (Asia/Singapore)	-	English	$750.00
May 12	09 AM - 01 PM (America/New_York)	-	English	$750.00
Jun 05	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jun 10	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jul 10	09 AM - 01 PM (Australia/Sydney)	-	English	$750.00
Jul 10	09 AM - 01 PM (America/New_York)	-	English	$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	May 12	11 AM - 03 PM (Asia/Singapore)	-	English	$750.00
May 12	09 AM - 01 PM (America/New_York)	-	English	$750.00
Jun 05	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jun 10	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jul 10	09 AM - 01 PM (Australia/Sydney)	-	English	$750.00
Jul 10	09 AM - 01 PM (America/New_York)	-	English	$750.00

Registration options

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Upcoming Public Classes

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This course teaches you how to design, build, and maintain a Databricks Genie Space, 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 fits into the Databricks AI/BI product family and how it translates natural language into reliable SQL queries. The course focuses on what it takes to create a Genie Space 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 Space 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 Spaces with business users through Databricks 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 Space that delivers governed, self-service conversational analytics at scale.

Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

Data Warehousing Practitioner

Data Warehousing with Databricks

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently using the industry-standard TCP-DI dataset as a reference. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

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

Apache Spark Developer

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Languages Available: English | 日本語 | 한국어