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	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

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	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

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Upcoming Public Classes

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This course covers performance optimization, cost control, and security for large-scale data warehousing deployments.This course is designed for Data Warehousing practitioners responsible for managing Databricks environments serving hundreds or thousands of users across multiple business units. You will gain the skills necessary to efficiently scale data warehousing operations while maintaining high performance, cost-effectiveness, and compliance with security standards.

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.

Building ETL Pipelines with SQL

This course teaches how to build production-ready ETL pipelines using pure SQL on the Databricks Data Intelligence Platform. Students learn Streaming Tables with Auto Loader for incremental ingestion, Materialized Views with incremental refresh for Silver-to-Gold transformations, AUTO CDC (FLOW AUTO CDC) for declarative SCD Type 1 and Type 2 dimension management, and Lakeflow Jobs with SQL File tasks for production orchestration. The course follows a realistic retail dataset through the medallion architecture (Bronze → Silver → Gold).

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.