Generative AI Solution Development

This course introduces contextual generative AI solutions using the retrieval-augmented generation (RAG) method. First, you’ll be introduced to RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, we’ll show you how to prepare data for generative AI solutions and connect this process with building an RAG architecture. Finally, you’ll explore concepts related to context, embedding vectors, vector databases, and the utilization of Mosaic AI Vector Search.

Note: This is the first course in the 'Generative AI Engineering with Databricks’ series.

Skill Level

Associate

Duration

Prerequisites

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

• Familiarity with the Databricks Data Intelligence Platform, specifically regarding Workspace navigation, Notebooks, and Compute resources.

• Intermediate proficiency in Python programming for data manipulation and interacting with SDKs or APIs.

• Understanding of core data management concepts, including Delta Lake tables and Unity Catalog governance structures.

• Basic knowledge of Generative AI fundamentals, such as Large Language Models (LLMs), hallucinations, and context windows.

• Familiarity with structured versus unstructured data and standard formatting styles like JSON and Markdown.

• Foundational understanding of machine learning concepts, including numerical vector representations and model serving.

• Basic understanding of REST APIs for querying endpoints and managing model interactions.

Self-Paced

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

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Self-Paced

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

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

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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

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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 Model Development

This comprehensive course provides a practical guide to developing traditional machine learning models on Databricks, emphasizing hands-on demonstrations and workflows using popular ML libraries. Participants will explore key ML techniques, including regression and clustering, while leveraging Databricks’ powerful capabilities. The course covers MLflow integration for model tracking, Databricks Feature Store for feature management, and Optuna for hyperparameter tuning. Additionally, participants will learn how to accelerate model training with Databricks AutoML. By the end of the course, learners will have real-world, practical skills to develop, optimize, and deploy machine learning models efficiently in the Databricks environment.

Note:

This is the second course in the 'Machine Learning with Databricks’ series.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!

Free

Associate

Data Warehousing Practitioner

Get Started with Databricks for Data Warehousing

This course provides a comprehensive overview of Databricks’ modern approach to data warehousing, highlighting how a data lakehouse architecture combines the strengths of traditional data warehouses with the flexibility and scalability of the cloud. You’ll learn about the AI-driven features that enhance data transformation and analysis on the Databricks Data Intelligence Platform. Designed for data warehousing practitioners, this course provides you with the foundational information needed to begin building and managing high-performant, AI-powered data warehouses on Databricks.

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!

Free

Onboarding

Data Architect

Get Started with Lakehouse Architecture on Databricks

In this course, you will explore the Databricks Data Intelligence Platform from the perspective of platform architecture, specifically related to the platform foundation in lakehouse architecture. You will learn about the scope, vision, and capabilities of a lakehouse-based platform, examine how Databricks integrates with major cloud providers, and discover the key features of a successful lakehouse implementation through the well-architected lakehouse framework. The course also covers essential architectural principles, best practices, and data architecture strategies to help accelerate your organization’s data and AI initiatives using Databricks.

Free

Onboarding