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There are 4 modules in this course
This course is designed for individuals at both an intermediate and beginner level, including data scientists, AI enthusiasts, and professionals seeking to harness the power of Azure for Large Language Models (LLMs). Tailored for those with foundational programming experience and familiarity with Azure basics, this comprehensive program takes you through a four-week journey. In the first week, you'll delve into Azure's AI services and the Azure portal, gaining insights into large language models, their functionalities, and strategies for risk mitigation. Subsequent weeks cover practical applications, including leveraging Azure Machine Learning, managing GPU quotas, deploying models, and utilizing the Azure OpenAI Service. As you progress, the course explores nuanced query crafting, Semantic Kernel implementation, and advanced strategies for optimizing interactions with LLMs within the Azure environment. The final week focuses on architectural patterns, deployment strategies, and hands-on application building using RAG, Azure services, and GitHub Actions workflows. Whether you're a data professional or AI enthusiast, this course equips you with the skills to deploy, optimize, and build robust large-scale applications leveraging Azure and Large Language Models.
In this module, you will learn how to get started with Azure and its AI services through an introduction to the Azure portal, and key offerings like Azure Machine Learning. You will also gain an understanding of large language models, including how they work, their benefits and risks, and strategies for mitigating those risks. Finally, you will be introduced to options for discovering, evaluating, and deploying pre-trained LLMs in Azure, including leveraging prompt engineering for responsible data grounding.
In this module, you will learn to leverage Azure for Large Language Models (LLMs) by using Azure Machine Learning through its compute resources and managing GPU quotas and model deployments as well as Azure OpenAI Service. You will apply this knowledge by deploying a model and using its inference API using the Python programming language.
What's included
18 videos4 readings1 assignment
Show info about module content
18 videos•Total 55 minutes
Introduction•1 minute
GPU quotas and availability•4 minutes
Creating a compute resource•4 minutes
Deploying the model•3 minutes
Using the inference API•4 minutes
Summary•1 minute
Introduction•1 minute
Getting access to Azure OpenAI Service•4 minutes
Creating an Azure OpenAI Service resource•5 minutes
Deploy an OpenAI model•5 minutes
Using the playground•5 minutes
Summary•1 minute
Introduction•1 minute
Using keys and endpoints•5 minutes
Creating a simple Python example•5 minutes
Reviewing usage and quotas•2 minutes
Cleaning up resources•4 minutes
Summary•1 minute
4 readings•Total 40 minutes
Azure ML: Create Resources•10 minutes
External lab: Create a compute resource•10 minutes
External lab: Use the Azure OpenAI Service Playground•10 minutes
External lab: Using Azure OpenAI APIs•10 minutes
1 assignment•Total 30 minutes
LLMs with Azure•30 minutes
Extending with Functions and Plugins
Module 3•2 hours to complete
Module details
In this module, you will discover the art of crafting nuanced queries for Large Language Models (LLMs) in Azure through the implementation of Semantic Kernel. You will gain insights into refining prompts, understand the dynamics of using system prompts, and explore advanced strategies to optimize your interaction with LLMs. You will apply these techniques hands-on to enhance your proficiency in leveraging Semantic Kernel within the Azure environment.
What's included
19 videos3 readings1 assignment
Show info about module content
19 videos•Total 61 minutes
Introduction•1 minute
What is Semantic Kernel?•4 minutes
Using Semantic Kernel with Azure•6 minutes
Using a system prompt•3 minutes
Advanced system prompts•3 minutes
Summary•1 minute
Introduction•1 minute
Overview of functions•4 minutes
Defining functions•6 minutes
Using the function with the LLM•4 minutes
Working with errors•4 minutes
Summary•1 minute
Introduction•1 minute
Creating a glue function•6 minutes
Consuming function arguments•3 minutes
Using a native function•4 minutes
Overview of a microservice for functions•3 minutes
Using an external microservice API•4 minutes
Summary•1 minute
3 readings•Total 30 minutes
External lab: Using Semantic Kernel with Azure•10 minutes
External lab: Using Functions•10 minutes
External lab: Use native functions•10 minutes
1 assignment•Total 30 minutes
Functions and Plugins•30 minutes
Building an End-to-End LLM application in Azure
Module 4•3 hours to complete
Module details
In this module, you will explore architectural patterns and deployment of large language model applications. By studying RAG, Azure services, and GitHub Actions, you will learn how to build robust applications. You will apply your learning by implementing RAG with Azure search, creating GitHub Actions workflows, and deploying an end-to-end application.
What's included
20 videos8 readings1 assignment
Show info about module content
20 videos•Total 72 minutes
Introduction•1 minute
Architectural overview•3 minutes
What is RAG•4 minutes
Overview of Azure AI Search•4 minutes
Automation and deployment with GitHub•4 minutes
Summary•1 minute
Introduction•1 minute
Create the Azure resources•5 minutes
Create the embeddings•5 minutes
Create and upload the index•2 minutes
Verifying the embeddings•3 minutes
Using RAG with Azure OpenAI•5 minutes
Summary•1 minute
Introduction•1 minute
Application overview•7 minutes
Setting up Azure components•4 minutes
Architectural overview•4 minutes
Using GitHub Actions with Azure•8 minutes
Verifying and troubleshooting deployments•6 minutes
Summary•2 minutes
8 readings•Total 80 minutes
External lab: Create an Azure AI Search resource•10 minutes
Azure AI Document Intelligence and Azure OpenAI•10 minutes
Introduction to RAG•10 minutes
External lab: Create embeddings in an index•10 minutes
Azure Container Apps•10 minutes
External Lab: Deploy an end-to-end application•10 minutes
Next steps•10 minutes
Share your learning experience•10 minutes
1 assignment•Total 30 minutes
End-to-end LLM applications•30 minutes
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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.