Generative AI Application Deployment and Monitoring

The course covers details about how to deploy, operationalize, and monitor generative AI applications. The course will help you gain skills in the deployment of generative AI applications using tools like Model Serving. It also covers how to operationalize generative AI applications following best practices and recommended architectures. Finally, the course discusses the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Skill Level

Associate

Duration

Prerequisites

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

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
Familiarity with Databricks Data Intelligence Platform tools for evaluation and governance.

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

See all our registration options

Registration options

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

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

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

Purchase now

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

Machine Learning Practitioner

Machine Learning with Databricks

Welcome to Machine Learning with Databricks!

This course is your gateway to mastering machine learning workflows on Databricks. Dive into data preparation, model development, deployment, and operations, guided by expert instructors. Learn essential skills for data exploration, model training, and deployment strategies tailored for Databricks. By the end of the course, you'll have the knowledge and confidence to navigate the entire machine learning lifecycle on the Databricks Platform, empowering you to build and deploy robust machine learning solutions efficiently.

Deploy Workloads with Databricks Workflows

By scheduling tasks with Databricks Jobs, applications can be run automatically to keep tables in the Lakehouse fresh. Using Databricks SQL to schedule updates to queries and dashboards allows quick insights using the newest data. In this course, students will be introduced to task orchestration using the Databricks Workflow Jobs UI. Optionally, they will configure and schedule dashboards and alerts to reflect updates to production data pipelines.

In this course, you’ll learn how to orchestrate data pipelines with Databricks Workflow Jobs and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with Databricks Workflows, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines.

Note: This course is part of the 'Data Engineering with Databricks' course series.

Free

Associate

Machine Learning Practitioner

Machine Learning Operations

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.

Free

Associate

Career Workshop/

March 20

Careers at Databricks

We're on a mission to help data teams solve the world's toughest problems. Will you join us?

Advance my career now

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.

FAQ