Implement Generative AI Engineering with Azure Databricks

MS-DP3028

Type

Virtual

Classroom ILT

Skill Level

Microsoft Techs: Intermediate

Available dates

Learning Path

Microsoft Azure

Virtual

Duration

1 Day

TYPE

Virtual

Classroom ILT

LEARNING PATH

Microsoft Azure

SKILL LEVEL

Microsoft Techs: Intermediate

DURATION

1 Day

AVAILABLE DATES

Available on request.

For more information click on button below

Course Type

Virtual Course (R3 500,00)

In-Person Course (R3 750,00)

Total Price: -

Introduction

This course covers generative AI engineering on Azure Databricks, using Spark to explore, fine-tune, evaluate, and integrate advanced language models. It teaches how to implement techniques like retrieval-augmented generation (RAG) and multi-stage reasoning, as well as how to fine-tune large language models for specific tasks and evaluate their performance. Students will also learn about responsible AI practices for deploying AI solutions and how to manage models in production using LLMOps (Large Language Model Operations) on Azure Databricks.

Audience Profile

This course is designed for software engineers who are responsible for creating cloud-native solutions using Azure Cosmos DB for NoSQL and its various SDKs.

Pre-requisite

Before attending this course, delegates must have:

Familiarity with familiar with fundamental Azure Databricks concepts

Course Objectives

After completion of this course, you will be able to:

Understand language models in Azure Databricks
Implement Retrieval Augmented Generation (RAG) with Azure Databricks
Implement multi-stage reasoning in Azure Databricks
Fine-tune language models with Azure Databricks
Implement LLMOps in Azure Databricks

Course Content

Module 1: Get started with language models in Azure Databricks

Learn how Large Language Models (LLMs) have revolutionized various industries by enabling advanced natural language processing (NLP) capabilities. These language models are utilized in a wide array of applications, including text summarization, sentiment analysis, language translation, zero-shot classification, and few-shot learning.

Lessons:

Introduction

Understand Generative AI

Understand Large Language Models (LLMs.)

Identify key components of LLM applications

Use LLMs for Natural Language Processing (NLP) tasks

Exercise – Explore language models

Module assessment

Summary

By the end of this module, you’ll be able to:

Describe Generative AI

Describe Large Language Models (LLMs)

Identify key components of LLM applications

Use LLMs for Natural Language Processing (NLP) tasks

Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks

Retrieval Augmented Generation (RAG) is an advanced technique in natural language processing that enhances the capabilities of generative models by integrating external information retrieval mechanisms. When you use both generative models and retrieval systems, RAG dynamically fetches relevant information from external data sources to augment the generation process, leading to more accurate and contextually relevant outputs.

Lessons:

Introduction

Explore the main concepts of a RAG workflow

Prepare your data for RAG

Find relevant data with vector search

Rerank your retrieved results

Exercise – Set up RAG

Module assessment

Summary

By the end of this module, you’ll be able to:

Set up a RAG workflow

Prepare your data for RAG

Retrieve relevant documents with vector search

Improve model accuracy by reranking your search results

Module 3: Implement multi-stage reasoning in Azure Databricks

Multi-stage reasoning systems break down complex problems into multiple stages or steps, with each stage focusing on a specific reasoning task. The output of one stage serves as the input for the next, allowing for a more structured and systematic approach to problem-solving.

Lessons:

Introduction

What are multi-stage reasoning systems?

Explore LangChain

Explore LlamaIndex

Explore Haystack

Explore the DSPy framework

Exercise – Implement multistage reasoning with LangChain

Module assessment

Summary

By the end of this module, you’ll be able to:

Identify the need for multistage reasoning systems

Describe a multi-stage reasoning workflow

Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework

Module 4: Fine-tune language models with Azure Databricks

Fine-tuning uses Large Language Models’ (LLMs) general knowledge to improve performance on specific tasks, allowing organizations to create specialized models that are more accurate and relevant while saving resources and time compared to training from scratch.

Lessons:

Introduction

What is fine-tuning?

Prepare your data for finetuning

Fine-tune an Azure OpenAI model

Exercise – Fine-tune an Azure OpenAI model

Module assessment

Summary

By the end of this module, you’ll be able to:

Understand when to use finetuning

Prepare your data for finetuning

Fine-tune an Azure OpenAI model

Module 5: Evaluate language models with Azure Databricks

In this module, you explore Large Language Model evaluation using various metrics and approaches, learn about evaluation challenges and best practices, and discover automated evaluation techniques including LLM-as-a-judge methods.

Lessons:

Introduction

Explore LLM evaluation

Evaluate LLMs and AI systems

Evaluate LLMs with standard metrics

Describe LLM-as-a-judge for evaluation

Exercise – Evaluate an Azure OpenAI model

Module assessment

Summary

By the end of this module, you’ll be able to:

Evaluate LLM evaluation models − Describe the relationship between LLM evaluation and AI system evaluation

Describe standard LLM evaluation metrics like accuracy, perplexity, and toxicity

Describe LLM-as-a-judge for evaluation

Module 6: Review responsible AI principles for language models in Azure Databricks

When working with Large Language Models (LLMs) in Azure Databricks, it’s important to understand the responsible AI principles for implementation, ethical considerations, and how to mitigate risks. Based on identified risks, learn how to implement key security tooling for language models.

Lessons:

Introduction

What is responsible AI?

Identify risks

Mitigate issues

Use key security tooling to protect your AI systems

Exercise – Implement responsible AI

Module assessment

Summary

By the end of this module, you’ll be able to:

Describe the responsible AI principles for implementation of language models

Identify the ethical considerations for language models

Mitigate the risks associated with language models

Implement key security tooling for language models

Module 7: Implement LLMOps in Azure Databricks

Streamline the implementation of Large Language Models (LLMs) with LLMOps (LLM Operations) in Azure Databricks. Learn how to deploy and manage LLMs throughout their lifecycle using Azure Databricks.

Lessons:

Introduction

Transition from traditional MLOps to LLMOps

Understand model deployments

Describe MLflow deployment capabilities

Use Unity Catalog to manage models

Exercise – Implement LLMOps

Module assessment

Summary

By the end of this module, you’ll be able to:

Describe the LLM lifecycle overview

Identify the model deployment option that best fits your needs

Use MLflow and Unity Catalog to implement LLMops

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Implement Generative AI Engineering with Azure Databricks

Type

Skill Level

Available dates

Learning Path

Duration

TYPE

Virtual

Classroom ILT

LEARNING PATH

SKILL LEVEL

DURATION

AVAILABLE DATES

Microsoft Overview

contact

Vendor
Authorised

Details

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newsletter

Implement Generative AI Engineering with Azure Databricks

Type

Skill Level

Available dates

Learning Path

Duration

TYPE

Virtual

Classroom ILT

LEARNING PATH

SKILL LEVEL

DURATION

AVAILABLE DATES

Microsoft Overview

contact

Vendor Authorised

Details

Menu

newsletter

Vendor
Authorised