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Noah Gift,
Alfredo Deza,
and
Derek Wales
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ac3ebu/operationalizing
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What's inside
Syllabus
Introduction to LLMOps with Azure
This week, 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.
Read more
Syllabus
Introduction to LLMOps with Azure
This week, 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.
Read more
LLMs with Azure
This week, 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.
Extending with Functions and Plugins
This week, 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.
Building an End-to-End LLM application in Azure
This week, 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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops foundational knowledge and skills for using Azure Large Language Models (LLMs) for industry applications
Teaches industry-standard techniques and best practices for deploying LLMs using Azure
Covers a wide range of topics in using LLMs with Azure, from basics to advanced techniques
Provides opportunities for hands-on practice through assignments and exercises
Emphasizes risk mitigation and responsible use of LLMs
Taught by experienced instructors who are recognized for their work in the field of LLMs
Save this course
Save Operationalizing LLMs on Azure to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Operationalizing LLMs on Azure with these
activities:
Review Azure Fundamentals
Show steps
This activity will help you refresh your knowledge of Azure fundamentals before starting this course.
Browse courses on
Azure Fundamentals
Show steps
-
Go through the Microsoft Learn Azure Fundamentals module.
-
Take the Azure Fundamentals certification exam.
Review Large Language Models (LLMs)
Show steps
Refresh your understanding of the fundamentals of LLMs, their capabilities, and limitations before starting the course.
Browse courses on
Large Language Models
Show steps
-
Revisit key concepts of LLMs, including their architecture, training process, and applications.
-
Explore different types of LLMs and their respective strengths and weaknesses.
-
Read articles or watch videos that provide an overview of LLMs.
Attend an Azure AI Meetup
Show steps
This activity will help you connect with other people who are interested in Azure AI.
Show steps
-
Find an Azure AI Meetup in your area.
-
Attend the Meetup and introduce yourself to other attendees.
-
Participate in the discussions and ask questions.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Azure Machine Learning Tutorial
Show steps
Familiarize yourself with the basics of Azure Machine Learning to enhance your understanding of the course content.
Browse courses on
Azure Machine Learning
Show steps
-
Follow the official Microsoft tutorial: https://docs.microsoft.com/en-us/azure/machine-learning/quickstart-create-train-deploy-model-designer
Follow Tutorials on Azure Machine Learning
Show steps
Enhance your practical skills by following guided tutorials that demonstrate how to use Azure Machine Learning services.
Browse courses on
Azure Machine Learning
Show steps
-
Find tutorials that cover Azure Machine Learning concepts relevant to the course.
-
Follow the tutorials step-by-step, experimenting with different parameters and data.
-
Build and deploy your own ML models using Azure Machine Learning.
Practice Azure Machine Learning Commands
Show steps
This activity will help you become more familiar with the Azure Machine Learning environment and its commands.
Browse courses on
Azure Machine Learning
Show steps
-
Log in to the Azure portal and create a new Azure Machine Learning workspace.
-
Create a new Python notebook in your workspace and import the Azure Machine Learning SDK.
-
Run through the Azure Machine Learning command reference and try out some of the most common commands.
Peer Discussion on RAG
Show steps
Engage with classmates to explore real-world use cases of RAG and its applications to deepen your comprehension.
Browse courses on
RAG
Show steps
-
Form a study group with other students
-
Identify a specific topic or project related to RAG
-
Discuss and brainstorm ideas
Azure OpenAI Service Tutorial
Show steps
Gain hands-on experience with the Azure OpenAI Service to complement your theoretical knowledge.
Browse courses on
Azure OpenAI Service
Show steps
-
Follow the official Microsoft tutorial: https://docs.microsoft.com/en-us/azure/openai-service/quickstart-deploy-model
Practice Query Crafting for LLMs
Show steps
Develop proficiency in crafting effective queries for LLMs to maximize the quality and relevance of their responses.
Show steps
-
Experiment with different query structures and formats to understand their impact on LLM responses.
-
Analyze LLM responses to identify patterns and improve query formulations.
-
Utilize tools or resources that provide guidance on query crafting for LLMs.
Deploy a Model with Azure OpenAI Service
Show steps
This activity will give you hands-on experience deploying a model using the Azure OpenAI Service.
Browse courses on
Azure OpenAI Service
Show steps
-
Follow the Azure OpenAI Service documentation to create a new deployment.
-
Configure your deployment settings, such as the model version and the number of instances.
-
Deploy your model and test it out by sending it some requests.
LLM Query Optimization Guide
Show steps
Deepen your understanding of LLM query optimization techniques and their practical applications.
Browse courses on
Query Optimization
Show steps
-
Research different query optimization approaches
-
Create a comprehensive guide outlining best practices and strategies
-
Share your guide with the class for feedback
Build a Chatbot with Azure Large Language Models
Show steps
This activity will allow you to apply your knowledge of Azure Large Language Models by building a chatbot.
Browse courses on
Chatbot Development
Show steps
-
Design the architecture of your chatbot, including the user interface and the flow of conversation.
-
Use the Azure Large Language Models to train your chatbot on a dataset of会話.
-
Deploy your chatbot and test it out with different users.
Interactive LLM Application Demo
Show steps
Showcase your understanding of LLM applications by designing and building a fully functional interactive demo.
Show steps
-
Plan the functionality and user interface of your demo
-
Integrate an LLM into your application
-
Develop a user-friendly interface
-
Test and refine your demo
Build an LLM-Powered Application
Show steps
Challenge yourself by building an end-to-end LLM-powered application, applying the knowledge and skills gained throughout the course.
Show steps
-
Choose a problem or domain that you are passionate about.
-
Design and develop your application, integrating LLMs to enhance its functionality.
-
Deploy your application and gather feedback to iterate and improve.
Personal LLM Assistant
Show steps
Apply your LLM knowledge to a practical project by creating a personalized assistant tailored to your specific needs.
Show steps
-
Define the scope and functionality of your assistant
-
Select an appropriate LLM and integrate it
-
Develop a user interface and interaction model
-
Deploy and test your assistant
Develop a Natural Language Processing Application
Show steps
This project will challenge you to use Azure Large Language Models to solve a real-world problem.
Browse courses on
Natural Language Processing
Show steps
-
Identify a problem that can be solved using natural language processing.
-
Design and develop a solution using Azure Large Language Models.
-
Deploy your solution and evaluate its performance.
Review Azure Fundamentals
Show steps
This activity will help you refresh your knowledge of Azure fundamentals before starting this course.
Browse courses on
Azure Fundamentals
Show steps
Show steps
Show steps
- Go through the Microsoft Learn Azure Fundamentals module.
- Take the Azure Fundamentals certification exam.
Review Large Language Models (LLMs)
Show steps
Refresh your understanding of the fundamentals of LLMs, their capabilities, and limitations before starting the course.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Revisit key concepts of LLMs, including their architecture, training process, and applications.
- Explore different types of LLMs and their respective strengths and weaknesses.
- Read articles or watch videos that provide an overview of LLMs.
Attend an Azure AI Meetup
Show steps
This activity will help you connect with other people who are interested in Azure AI.
Show steps
Show steps
Show steps
- Find an Azure AI Meetup in your area.
- Attend the Meetup and introduce yourself to other attendees.
- Participate in the discussions and ask questions.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Azure Machine Learning Tutorial
Show steps
Familiarize yourself with the basics of Azure Machine Learning to enhance your understanding of the course content.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Follow the official Microsoft tutorial: https://docs.microsoft.com/en-us/azure/machine-learning/quickstart-create-train-deploy-model-designer
Follow Tutorials on Azure Machine Learning
Show steps
Enhance your practical skills by following guided tutorials that demonstrate how to use Azure Machine Learning services.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Find tutorials that cover Azure Machine Learning concepts relevant to the course.
- Follow the tutorials step-by-step, experimenting with different parameters and data.
- Build and deploy your own ML models using Azure Machine Learning.
Practice Azure Machine Learning Commands
Show steps
This activity will help you become more familiar with the Azure Machine Learning environment and its commands.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Log in to the Azure portal and create a new Azure Machine Learning workspace.
- Create a new Python notebook in your workspace and import the Azure Machine Learning SDK.
- Run through the Azure Machine Learning command reference and try out some of the most common commands.
Peer Discussion on RAG
Show steps
Engage with classmates to explore real-world use cases of RAG and its applications to deepen your comprehension.
Browse courses on
RAG
Show steps
Show steps
Show steps
- Form a study group with other students
- Identify a specific topic or project related to RAG
- Discuss and brainstorm ideas
Azure OpenAI Service Tutorial
Show steps
Gain hands-on experience with the Azure OpenAI Service to complement your theoretical knowledge.
Browse courses on
Azure OpenAI Service
Show steps
Show steps
Show steps
- Follow the official Microsoft tutorial: https://docs.microsoft.com/en-us/azure/openai-service/quickstart-deploy-model
Practice Query Crafting for LLMs
Show steps
Develop proficiency in crafting effective queries for LLMs to maximize the quality and relevance of their responses.
Show steps
Show steps
Show steps
- Experiment with different query structures and formats to understand their impact on LLM responses.
- Analyze LLM responses to identify patterns and improve query formulations.
- Utilize tools or resources that provide guidance on query crafting for LLMs.
Deploy a Model with Azure OpenAI Service
Show steps
This activity will give you hands-on experience deploying a model using the Azure OpenAI Service.
Browse courses on
Azure OpenAI Service
Show steps
Show steps
Show steps
- Follow the Azure OpenAI Service documentation to create a new deployment.
- Configure your deployment settings, such as the model version and the number of instances.
- Deploy your model and test it out by sending it some requests.
LLM Query Optimization Guide
Show steps
Deepen your understanding of LLM query optimization techniques and their practical applications.
Browse courses on
Query Optimization
Show steps
Show steps
Show steps
- Research different query optimization approaches
- Create a comprehensive guide outlining best practices and strategies
- Share your guide with the class for feedback
Build a Chatbot with Azure Large Language Models
Show steps
This activity will allow you to apply your knowledge of Azure Large Language Models by building a chatbot.
Browse courses on
Chatbot Development
Show steps
Show steps
Show steps
- Design the architecture of your chatbot, including the user interface and the flow of conversation.
- Use the Azure Large Language Models to train your chatbot on a dataset of会話.
- Deploy your chatbot and test it out with different users.
Interactive LLM Application Demo
Show steps
Showcase your understanding of LLM applications by designing and building a fully functional interactive demo.
Show steps
Show steps
Show steps
- Plan the functionality and user interface of your demo
- Integrate an LLM into your application
- Develop a user-friendly interface
- Test and refine your demo
Build an LLM-Powered Application
Show steps
Challenge yourself by building an end-to-end LLM-powered application, applying the knowledge and skills gained throughout the course.
Show steps
Show steps
Show steps
- Choose a problem or domain that you are passionate about.
- Design and develop your application, integrating LLMs to enhance its functionality.
- Deploy your application and gather feedback to iterate and improve.
Personal LLM Assistant
Show steps
Apply your LLM knowledge to a practical project by creating a personalized assistant tailored to your specific needs.
Show steps
Show steps
Show steps
- Define the scope and functionality of your assistant
- Select an appropriate LLM and integrate it
- Develop a user interface and interaction model
- Deploy and test your assistant
Develop a Natural Language Processing Application
Show steps
This project will challenge you to use Azure Large Language Models to solve a real-world problem.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Identify a problem that can be solved using natural language processing.
- Design and develop a solution using Azure Large Language Models.
- Deploy your solution and evaluate its performance.
Career center
Learners who complete Operationalizing LLMs on Azure will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They then use this information to make recommendations and predictions. This course may be useful in providing a foundation for understanding the role of LLMs in data analysis and how to use them to collect, clean, and analyze data to identify trends and patterns.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to make recommendations and predictions. This course may be useful in providing a foundation for understanding the role of LLMs in data science and how to use them to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. They work with data scientists to understand the business problem and then design and implement machine learning solutions. This course may be useful in providing a foundation for understanding the role of LLMs in machine learning and how to use them to build and deploy machine learning models.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be useful in providing a foundation for understanding the role of LLMs in product management and how to use them to develop and launch new products.
Systems Architect
Systems Architect
Systems Architects envision and create the overall architecture of a system, including data storage, security, and user experience. They may also manage teams of engineers and oversee the implementation and maintenance of the system. This course may be useful in providing a foundation for understanding the role of LLMs in complex systems and how to integrate them effectively.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. They work with clients to understand their business needs and then design and implement cloud solutions that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in cloud computing and how to integrate them effectively.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work with users to understand their needs and then design and implement software solutions that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in software development and how to use them to build and maintain software systems.
Information Architect
Information Architect
Information Architects design and organize information systems. They work with users to understand their needs and then design and implement systems that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in information architecture and how to use them to design and organize information systems.
Trainer
Trainer
Trainers teach people how to use software and other technical products. They work with learners to understand their needs and then develop and deliver training materials that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in training and how to use them to develop and deliver training materials.
Web Developer
Web Developer
Web Developers design and develop websites. They work with clients to understand their needs and then design and develop websites that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in web development and how to use them to design and develop websites.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and other technical experts to understand the product and then write documentation that is clear and easy to understand. This course may be useful in providing a foundation for understanding the role of LLMs in technical writing and how to use them to create documentation for software and other technical products.
User Experience Designer
User Experience Designer
User Experience Designers design the user interface for software and other products. They work with users to understand their needs and then design interfaces that are easy to use and visually appealing. This course may be useful in providing a foundation for understanding the role of LLMs in user experience design and how to use them to design user interfaces that are easy to use and visually appealing.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. They work with stakeholders to understand their needs and then design and implement solutions that meet those needs. This course may be useful in providing a foundation for understanding the role of LLMs in business analysis and how to use them to analyze business processes and identify opportunities for improvement.
Consultant
Consultant
Consultants provide advice to businesses on how to improve their operations. They work with clients to identify problems and then develop and implement solutions. This course may be useful in providing a foundation for understanding the role of LLMs in consulting and how to use them to provide advice to businesses on how to improve their operations.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with stakeholders to define project goals and objectives and then develop and implement plans to achieve those goals. This course may be useful in providing a foundation for understanding the role of LLMs in project management and how to use them to plan and execute projects.
For more career information including salaries, visit:
OpenCourser.com/course/ac3ebu/operationalizing
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Operationalizing LLMs on Azure.
Provides a comprehensive overview of deep learning for NLP. It covers a wide range of topics, including word embeddings, recurrent neural networks, and transformers.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including the different types of deep learning models, the challenges of developing deep learning systems, and the applications of deep learning.
Provides a comprehensive overview of statistical learning, which is the foundation of many machine learning algorithms. It valuable resource for anyone who wants to learn the theoretical underpinnings of machine learning.
Save
Provides a comprehensive overview of the history of AI. It covers a wide range of topics, including the different approaches to AI development, the challenges of developing AI systems, and the implications of AI for the future of work and society.
Provides a practical guide to machine learning with R. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Provides a practical guide to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Provides a comprehensive overview of NLP, with a focus on Python implementations. It valuable resource for anyone who wants to learn how to use Python for NLP tasks.
Provides a collection of recipes for common machine learning tasks in Python. It valuable resource for anyone who wants to learn how to use Python for machine learning tasks.
Provides a gentle introduction to machine learning. It valuable resource for anyone who wants to learn the basics of machine learning without getting bogged down in the details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ac3ebu/operationalizing
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Effort
5 weeks, 5 hours/week
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Derek Wales
Language
English
Transcripts
Español
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Português
Deutsch
Italiano
中文
العربية
한국어
日本語
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This course belongs to a
collection
called
Large Language Model Operations (LLMOps). It's comprised of five courses:
- Databricks to Local LLMs
- Open Source LLMOps Solutions
- Operationalizing LLMs on Azure (viewing)
- GenAI and LLMs on AWS
- Advanced Data Engineering
Good to know
Develops foundational knowledge and skills for using Azure Large Language Models (LLMs) for industry applications
Teaches industry-standard techniques and best practices for deploying LLMs using Azure
Covers a wide range of topics in using LLMs with Azure, from basics to advanced techniques
Provides opportunities for hands-on practice through assignments and exercises
Emphasizes risk mitigation and responsible use of LLMs
Taught by experienced instructors who are recognized for their work in the field of LLMs
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