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 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

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Upcoming Public Classes

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

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Upcoming Public Classes

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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.

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Machine Learning Practitioner

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This course is aimed at data scientists and machine learning practitioners and consists of two, four-hours modules.

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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.

Advanced Machine Learning Operations

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.