Generative AI Solution Development
This is your introduction to 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 a RAG architecture. Finally, you’ll explore concepts related to context embedding, vectors, vector databases, and the utilization of Mosaic AI Vector Search.
Languages Available: English | 日本語 | Português BR | 한국어
- Familiarity with natural language processing concepts
- Familiarity with prompt engineering/prompt engineering best practices
- Familiarity with the Databricks Data Intelligence Platform
Outline
Introduction to RAG
- What is RAG?
- In Context Learning with AI Playground
Preparing Data for RAG Solutions
- Data Storage and Governance
- Data Extraction and Chunking
- Embedding Model
- Data Preparation in Databricks
Vector Search
- Introduction to Vector Stores
- Vector Search Process and Performance
- Choosing the right Vector Database
- Mosaic AI Vector Search
- Creating a Vector Search Index
Assembling and Evaluating a RAG Application
- MLflow
- Evaluating a RAG Application and Continual Learning
- Assembling a RAG Application
Upcoming Public Classes
Date | Time | Language | Price |
|---|---|---|---|
May 08 | 09 AM - 01 PM (Europe/London) | English | $750.00 |
Jul 08 | 09 AM - 01 PM (America/New_York) | English | $750.00 |
Jul 09 | 09 AM - 01 PM (Asia/Kolkata) | English | $750.00 |
Jul 10 | 01 PM - 05 PM (Europe/London) | 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.
Private Class Request
If your company is interested in private training, please submit a request.
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