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Noah Gift
and
Alfredo Deza
Master Large Language Model Operations on Azure
Read more
Master Large Language Model Operations on Azure
Read more
Master Large Language Model Operations on Azure
- Unlock Azure's full potential for deploying & optimizing Large Language Models (LLMs)
- Build robust LLM applications leveraging Azure Machine Learning & OpenAI Service
- Implement architectural patterns & GitHub Actions workflows for streamlined MLOps
Course Highlights:
- Explore Azure AI services and LLM capabilities
- Mitigate risks with foundational strategies
- Leverage Azure ML for model deployment & management
- Optimize GPU quotas for performance & cost-efficiency
- Craft advanced queries for enriched LLM interactions
- Implement Semantic Kernel for enhanced query results
- Dive into architectural patterns like RAG for scalable architectures
- Build end-to-end LLM apps using Azure services & GitHub Actions
Ideal for data professionals, AI enthusiasts & Azure users looking to harness cutting-edge language AI capabilities. Gain practical MLOps skills through tailored modules & hands-on projects.
Register for this course and see more details by visiting:
OpenCourser.com/course/ckveew/large
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What's inside
Learning objectives
- Gain proficiency in leveraging azure for deploying and managing large language models (llms).
- Develop advanced query crafting skills using semantic kernel to optimize interactions with llms within the azure environment.
- Acquire hands-on experience in implementing patterns and deploying applications with retrieval augmented generation (rag)
- Gain proficiency in leveraging azure for deploying and managing large language models (llms).
- Develop advanced query crafting skills using semantic kernel to optimize interactions with llms within the azure environment.
- Acquire hands-on experience in implementing patterns and deploying applications with retrieval augmented generation (rag)
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers advanced query crafting skills using Semantic Kernel to optimize interactions with LLMs within the Azure environment
Examines practical techniques for optimizing LLM deployments and managing GPU quotas for performance and cost-efficiency
Provides hands-on experience with implementing advanced patterns and deploying applications with Retrieval Augmented Generation (RAG)
Taught by recognized industry experts Noah Gift and Alfredo Deza, who bring deep expertise in Large Language Model operations on Azure
Leverages Azure Machine Learning and OpenAI Service, industry-leading platforms for LLM development and deployment
Emphasizes foundational strategies for mitigating risks and ensuring responsible LLM usage
Save this course
Save Large Language Models with 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 Large Language Models with Azure with these
activities:
Review of Azure AI Services and LLM Capabilities
Show steps
Ensure a strong foundation by refreshing your knowledge of Azure's AI services and LLM capabilities.
Browse courses on
Azure AI Services
Show steps
-
Review Course Materials
-
Complete Knowledge Check Exercises
Organize Course Materials
Show steps
Lay a solid foundation for your learning by organizing notes, assignments, quizzes, and exams.
Show steps
-
Establish a System for Organization
-
Categorize and Label Materials
-
Create a Master List of All Materials
Review Azure ML Basics
Show steps
Prepare for Success by Revising the Essential Concepts of Azure ML
Show steps
-
Revisit the core concepts of Azure ML, including its architecture, services, and tools.
-
Review key algorithms and techniques used in machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.
-
Explore real-world applications of Azure ML in various industries.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Attend an Azure AI community event or webinar
Show steps
Connecting with experts and professionals in the Azure AI community will broaden your knowledge and provide insights into industry trends.
Show steps
-
Identify and register for relevant events or webinars
-
Attend the event and actively participate in discussions
-
Network with speakers, attendees, and Azure AI experts
Explore Azure ML and OpenAI Services
Show steps
Expand your knowledge of Azure's LLM services through guided tutorials.
Show steps
-
Identify Relevant Tutorials
-
Complete Selected Tutorials
-
Document Notable Concepts
Explore use cases of LLMs on Azure
Show steps
Broadening your knowledge of real-world applications for LLMs on Azure will foster a deeper understanding of their capabilities.
Browse courses on
Large Language Models
Show steps
-
Identify relevant case studies and examples
-
Read and analyze case studies to understand the benefits and challenges of using LLMs
-
Attend webinars or workshops on LLM applications
Practice deploying Azure Machine Learning models
Show steps
Deploying your models through Azure Machine Learning will reinforce your understanding of model management and optimization techniques.
Browse courses on
Azure Machine Learning
Show steps
-
Create a new Azure Machine Learning workspace
-
Create and train a model in your workspace
-
Deploy trained model as a web service
-
Test endpoint using sample data
Explore Semantic Kernel with OpenAI Quickstart
Show steps
Enhance Your Understanding of Semantic Kernel through Hands-on Practice
Browse courses on
Semantic Kernel
Show steps
-
Complete the OpenAI Quickstart tutorial on Semantic Kernel.
-
Experiment with different queries and explore the capabilities of Semantic Kernel.
-
Share your experiences and discuss best practices in using Semantic Kernel.
Peer Practice for LLM Deployment
Show steps
Collaborate with peers to strengthen your understanding of LLM deployment strategies.
Show steps
-
Form Study Groups
-
Share Knowledge and Experiences
-
Simulate Deployment Scenarios
Develop an LLM application using Azure services
Show steps
Building an LLM application will provide hands-on experience in implementing and integrating LLM capabilities with Azure services.
Show steps
-
Identify a problem or need that can be addressed with an LLM
-
Design the architecture and workflow of the application
-
Develop and deploy the application on Azure using appropriate services
-
Test and evaluate the application
Optimize GPU Quotas for Performance
Show steps
Gain hands-on experience in fine-tuning your Azure environment for optimal LLM performance.
Show steps
-
Monitor and Analyze Resource Usage
-
Identify Bottlenecks and Limitations
-
Create and Implement Optimization Plan
-
Evaluate and Adjust Optimization Strategies
Practice Implementing RAG Architecture
Show steps
Solidify Your Understanding of RAG Architecture through Practical Implementation
Browse courses on
Large Language Models
Show steps
-
Set up a development environment for implementing RAG architecture.
-
Build a small-scale RAG model using a pre-trained LLM.
-
Evaluate the performance of your RAG model and identify areas for improvement.
Develop a LLM Application Demo
Show steps
Solidify your understanding by building a real-world application using Azure's LLMs.
Show steps
-
Identify a Problem or Use Case
-
Design the LLM Solution
-
Develop and Implement the Application
-
Test and Evaluate the Application
Discuss LLM MLOps Best Practices
Show steps
Enhance Your Knowledge by Engaging in Peer Discussions on LLM MLOps
Browse courses on
Machine Learning Operations
Show steps
-
Form a study group or join an online community focused on LLM MLOps.
-
Share your experiences and ideas on implementing MLOps best practices for LLMs.
-
Collaborate on developing guidelines and recommendations for effective LLM MLOps.
Build an End-to-End LLM Application
Show steps
Demonstrate Your Mastery by Creating a Functional LLM Application
Show steps
-
Identify a real-world problem that can be solved using an LLM.
-
Design and develop an LLM application that addresses the identified problem.
-
Deploy your LLM application on Azure and integrate GitHub Actions for continuous integration.
Contribute to Open-Source LLM Projects
Show steps
Gain Practical Experience and Make Valuable Contributions to the LLM Community
Browse courses on
Open Source
Show steps
-
Identify open-source LLM projects that align with your interests.
-
Review the documentation and contribute in areas where you can add value.
-
Collaborate with other contributors to enhance the project's capabilities.
Review of Azure AI Services and LLM Capabilities
Show steps
Ensure a strong foundation by refreshing your knowledge of Azure's AI services and LLM capabilities.
Browse courses on
Azure AI Services
Show steps
Show steps
Show steps
- Review Course Materials
- Complete Knowledge Check Exercises
Organize Course Materials
Show steps
Lay a solid foundation for your learning by organizing notes, assignments, quizzes, and exams.
Show steps
Show steps
Show steps
- Establish a System for Organization
- Categorize and Label Materials
- Create a Master List of All Materials
Review Azure ML Basics
Show steps
Prepare for Success by Revising the Essential Concepts of Azure ML
Show steps
Show steps
Show steps
- Revisit the core concepts of Azure ML, including its architecture, services, and tools.
- Review key algorithms and techniques used in machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.
- Explore real-world applications of Azure ML in various industries.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Attend an Azure AI community event or webinar
Show steps
Connecting with experts and professionals in the Azure AI community will broaden your knowledge and provide insights into industry trends.
Show steps
Show steps
Show steps
- Identify and register for relevant events or webinars
- Attend the event and actively participate in discussions
- Network with speakers, attendees, and Azure AI experts
Explore Azure ML and OpenAI Services
Show steps
Expand your knowledge of Azure's LLM services through guided tutorials.
Show steps
Show steps
Show steps
- Identify Relevant Tutorials
- Complete Selected Tutorials
- Document Notable Concepts
Explore use cases of LLMs on Azure
Show steps
Broadening your knowledge of real-world applications for LLMs on Azure will foster a deeper understanding of their capabilities.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Identify relevant case studies and examples
- Read and analyze case studies to understand the benefits and challenges of using LLMs
- Attend webinars or workshops on LLM applications
Practice deploying Azure Machine Learning models
Show steps
Deploying your models through Azure Machine Learning will reinforce your understanding of model management and optimization techniques.
Browse courses on
Azure Machine Learning
Show steps
Show steps
Show steps
- Create a new Azure Machine Learning workspace
- Create and train a model in your workspace
- Deploy trained model as a web service
- Test endpoint using sample data
Explore Semantic Kernel with OpenAI Quickstart
Show steps
Enhance Your Understanding of Semantic Kernel through Hands-on Practice
Browse courses on
Semantic Kernel
Show steps
Show steps
Show steps
- Complete the OpenAI Quickstart tutorial on Semantic Kernel.
- Experiment with different queries and explore the capabilities of Semantic Kernel.
- Share your experiences and discuss best practices in using Semantic Kernel.
Peer Practice for LLM Deployment
Show steps
Collaborate with peers to strengthen your understanding of LLM deployment strategies.
Show steps
Show steps
Show steps
- Form Study Groups
- Share Knowledge and Experiences
- Simulate Deployment Scenarios
Develop an LLM application using Azure services
Show steps
Building an LLM application will provide hands-on experience in implementing and integrating LLM capabilities with Azure services.
Show steps
Show steps
Show steps
- Identify a problem or need that can be addressed with an LLM
- Design the architecture and workflow of the application
- Develop and deploy the application on Azure using appropriate services
- Test and evaluate the application
Optimize GPU Quotas for Performance
Show steps
Gain hands-on experience in fine-tuning your Azure environment for optimal LLM performance.
Show steps
Show steps
Show steps
- Monitor and Analyze Resource Usage
- Identify Bottlenecks and Limitations
- Create and Implement Optimization Plan
- Evaluate and Adjust Optimization Strategies
Practice Implementing RAG Architecture
Show steps
Solidify Your Understanding of RAG Architecture through Practical Implementation
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Set up a development environment for implementing RAG architecture.
- Build a small-scale RAG model using a pre-trained LLM.
- Evaluate the performance of your RAG model and identify areas for improvement.
Develop a LLM Application Demo
Show steps
Solidify your understanding by building a real-world application using Azure's LLMs.
Show steps
Show steps
Show steps
- Identify a Problem or Use Case
- Design the LLM Solution
- Develop and Implement the Application
- Test and Evaluate the Application
Discuss LLM MLOps Best Practices
Show steps
Enhance Your Knowledge by Engaging in Peer Discussions on LLM MLOps
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Form a study group or join an online community focused on LLM MLOps.
- Share your experiences and ideas on implementing MLOps best practices for LLMs.
- Collaborate on developing guidelines and recommendations for effective LLM MLOps.
Build an End-to-End LLM Application
Show steps
Demonstrate Your Mastery by Creating a Functional LLM Application
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using an LLM.
- Design and develop an LLM application that addresses the identified problem.
- Deploy your LLM application on Azure and integrate GitHub Actions for continuous integration.
Contribute to Open-Source LLM Projects
Show steps
Gain Practical Experience and Make Valuable Contributions to the LLM Community
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source LLM projects that align with your interests.
- Review the documentation and contribute in areas where you can add value.
- Collaborate with other contributors to enhance the project's capabilities.
Career center
Learners who complete Large Language Models with Azure will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers use LLMs to develop and deploy AI applications. This course will provide you with the skills to use LLMs to master Large Language Model Operations on Azure. You will also learn how to explore Azure AI services and LLM capabilities, craft advanced queries for enriched LLM interactions, and implement Semantic Kernel for enhanced query results.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use LLMs to build and deploy machine learning models. This course will provide you with the skills to use LLMs to build robust LLM applications using Azure Machine Learning & OpenAI Service. You will also learn how to implement architectural patterns & GitHub Actions workflows for streamlined MLOps.
Data Scientist
Data Scientist
Data Scientists use Large Language Models (LLMs) to process large amounts of data to extract insights. This course will provide you with the skills to use LLMs to unlock the full potential of Azure for deploying and managing LLMs. You will also learn how to mitigate risks with foundational strategies, leverage Azure ML for model deployment & management, and optimize GPU quotas for performance & cost-efficiency.
Data Analyst
Data Analyst
Data Analysts use LLMs to analyze large amounts of data to identify trends and patterns. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to acquire hands-on experience in implementing patterns and deploying applications with Retrieval Augmented Generation (RAG).
Research Scientist
Research Scientist
Research Scientists use LLMs to develop new AI algorithms and applications. This course will provide you with the skills to use LLMs to develop advanced query crafting skills using Semantic Kernel to optimize interactions with LLMs within the Azure environment. You will also learn how to dive into architectural patterns like RAG for scalable architectures.
Software Engineer
Software Engineer
Software Engineers use LLMs to develop and deploy software applications. This course will provide you with the skills to use LLMs to build end-to-end LLM apps using Azure services & GitHub Actions. You will also learn how to implement patterns and deploy applications with Retrieval Augmented Generation (RAG).
Product Manager
Product Manager
Product Managers use LLMs to develop and manage AI products. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to mitigate risks with foundational strategies, leverage Azure ML for model deployment & management, and optimize GPU quotas for performance & cost-efficiency.
Business Analyst
Business Analyst
Business Analysts use LLMs to analyze business data and identify opportunities for improvement. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to acquire hands-on experience in implementing patterns and deploying applications with Retrieval Augmented Generation (RAG).
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use LLMs to develop and deploy financial models. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to mitigate risks with foundational strategies, leverage Azure ML for model deployment & management, and optimize GPU quotas for performance & cost-efficiency.
Entrepreneur
Entrepreneur
Entrepreneurs use LLMs to develop and launch new businesses. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to acquire hands-on experience in implementing patterns and deploying applications with Retrieval Augmented Generation (RAG).
Consultant
Consultant
Consultants use LLMs to help their clients solve business problems. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to mitigate risks with foundational strategies, and craft advanced queries for enriched LLM interactions.
Journalist
Journalist
Journalists use LLMs to research and write articles. This course will provide you with the skills to use LLMs to craft advanced queries for enriched LLM interactions. You will also learn how to implement Semantic Kernel for enhanced query results.
Freelance Writer
Freelance Writer
Freelance Writers use LLMs to write articles, blog posts, and other content. This course will provide you with the skills to use LLMs to craft advanced queries for enriched LLM interactions. You will also learn how to implement Semantic Kernel for enhanced query results.
Salesperson
Salesperson
Salespeople use LLMs to generate leads and close deals. This course will provide you with the skills to use LLMs to craft advanced queries for enriched LLM interactions. You will also learn how to implement Semantic Kernel for enhanced query results.
Marketer
Marketer
Marketers use LLMs to create and manage marketing campaigns. This course will provide you with the skills to use LLMs to gain proficiency in leveraging Azure for deploying and managing Large Language Models (LLMs). You will also learn how to mitigate risks with foundational strategies.
For more career information including salaries, visit:
OpenCourser.com/course/ckveew/large
Reading list
We've selected ten 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
Large Language Models with Azure.
Provides a practical guide to using Python for deep learning. It valuable resource for those who want to learn more about the theory and practice of deep learning using Python.
Provides a practical guide to using PyTorch for deep learning. It valuable resource for those who want to learn more about the theory and practice of deep learning using PyTorch.
Save
Provides a comprehensive overview of machine learning algorithms. It valuable resource for those who want to learn more about the theory and practice of machine learning algorithms.
Provides a comprehensive overview of pattern recognition and machine learning. It valuable resource for those who want to learn more about the theory and practice of pattern recognition and machine learning.
Save
Comprehensive textbook on deep learning, which is the foundation of LLMs. It valuable resource for those who want to understand the theory and practice of deep learning.
Classic textbook on statistical learning. It valuable resource for those who want to understand the theory and practice of statistical learning.
Classic textbook on reinforcement learning. It valuable resource for those who want to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of NLP techniques and algorithms. It valuable resource for those who want to learn more about the theory and practice of NLP.
Provides a broad overview of AI and its applications. It good starting point for those who are new to the field and want to learn more about the potential of AI.
Provides a comprehensive overview of speech and language processing. It valuable resource for those who want to learn more about the theory and practice of speech and language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ckveew/large
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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Noah Gift
Alfredo Deza
Session
March 15, 2024
- March 16, 2028
Language
English
Transcript
English
Good to know
Covers advanced query crafting skills using Semantic Kernel to optimize interactions with LLMs within the Azure environment
Examines practical techniques for optimizing LLM deployments and managing GPU quotas for performance and cost-efficiency
Provides hands-on experience with implementing advanced patterns and deploying applications with Retrieval Augmented Generation (RAG)
Taught by recognized industry experts Noah Gift and Alfredo Deza, who bring deep expertise in Large Language Model operations on Azure
Leverages Azure Machine Learning and OpenAI Service, industry-leading platforms for LLM development and deployment
Emphasizes foundational strategies for mitigating risks and ensuring responsible LLM usage
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