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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

Skill Level
Associate
Duration
4h
Prerequisites
  • 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
Feb 17
08 AM - 12 PM (America/New_York)
English
$750.00
Feb 19
01 PM - 05 PM (Europe/London)
English
$750.00
Feb 21
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
Feb 27
09 AM - 01 PM (Europe/Paris)
Italian
$750.00
Mar 11
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
Mar 11
09 AM - 01 PM (Europe/London)
English
$750.00
Mar 13
01 PM - 05 PM (America/New_York)
English
$750.00
Apr 14
01 PM - 05 PM (Europe/London)
English
$750.00
Apr 16
09 AM - 01 PM (America/New_York)
English
$750.00
Apr 18
01 PM - 05 PM (Asia/Kolkata)
English
$750.00

Public Class Registration

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Upcoming Public Classes

Machine Learning Practitioner

Advanced Machine Learning with Databricks

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

Machine Learning at Scale

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.

Paid
8h
Lab
instructor-led
Professional

Questions?

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