Introduction
Uncover the necessities of crafting Generative AI functions by means of Microsoft’s Generative AI 18-lesson course for novices. Every phase contains a succinct video introduction, detailed written classes out there within the README, and Python and TypeScript Code Samples appropriate with Azure OpenAI and OpenAI API. Moreover, entry supplementary assets to additional improve your data on this dynamic area.
Every lesson covers its matter, so begin wherever you desire to. Classes are labeled both “Be taught” classes explaining a Generative AI idea or “Construct” classes explaining an idea and code examples in Python and TypeScript when attainable. Every lesson additionally features a “Hold Studying” part with extra studying instruments. Additional, on this weblog you’re going to get to know 18 Microsoft free programs on Gen AI
Conditions
- Entry to the Azure OpenAI Service or OpenAI API – Solely required to finish coding classes
- Fundamental data of Python or Typescript is useful – For absolute novices try these Python and TypeScript programs.
- A Github Account to fork this complete repo to your individual GitHub account
They’ve created a Course Setup lesson that can assist you with organising your developement atmosphere.
Establishing for the Microsoft Free Programs
Fork this Repo
Fork this complete repo to your individual GitHub account to have the ability to change any code and full the challenges. You can even star (🌟) this repo to seek out it and associated repos simpler.
Create a Codespace
To keep away from any dependency points when operating the code, we suggest operating this course in a GitHub codespace.
This may be created by choosing the Code choice in your forked model of this repo and choosing the Codespaces choice.
Storing Your API Keys
Maintaining your API keys secure and safe is necessary when constructing any kind of software. We encourage you to not retailer any API keys immediately within the code you might be working with as committing these particulars to a public repository may lead to undesirable prices and points.
Learn how to Run domestically in your pc
To run the code domestically in your pc, you would want to have some model of Python put in.
To then use the repository, you’ll want to clone it:
git clone https://github.com/microsoft/generative-ai-for-beginners
cd generative-ai-for-beginnersNow you have got every part checked out and may begin studying and work with the code.
Microsoft Free Programs for Gen AI Studying
Listed below are the Microsoft Free Programs on Gen AI you could know:
Course 1: Introduction to Generative AI and LLMs
This lesson will cowl:
- Introduction to the enterprise situation: our startup thought and mission.
- Generative AI and the way we landed on the present expertise panorama.
- Inside working of a giant language mannequin.
- Most important capabilities and sensible use instances of Massive Language Fashions.
Studying Objectives
After finishing this lesson, you’ll perceive:
- What generative AI is and the way Massive Language Fashions work.
- How one can leverage massive language fashions for various use instances, with a concentrate on training situations.
Course 2: Exploring and evaluating totally different LLMs
This lesson will cowl:
- Various kinds of LLMs within the present panorama.
- Testing, iterating, and evaluating totally different fashions in your use case in Azure.
- Learn how to deploy an LLM.
Studying Objectives
After finishing this lesson, it is possible for you to to:
- Choose the suitable mannequin in your use case.
- Perceive the best way to take a look at, iterate, and enhance efficiency of your mannequin.
- Know the way companies deploy fashions.
Course 3: Utilizing Generative AI Responsibly
This lesson will cowl:
- Why you must prioritize Accountable AI when constructing Generative AI functions.
- Core ideas of Accountable AI and the way they relate to Generative AI.
- Learn how to put these Accountable AI ideas into apply by means of technique and tooling.
Studying Objectives
After finishing this lesson you’ll know:
- The significance of Accountable AI when constructing Generative AI functions.
- When to suppose and apply the core ideas of Accountable AI when constructing Generative AI functions.
- What instruments and methods can be found to you to place the idea of Accountable AI into apply.
Course 4: Understanding Immediate Engineering Fundamentals
On this lesson, we be taught what Immediate Engineering is, why it issues, and the way we are able to craft simpler prompts for a given mannequin and software goal. We’ll perceive core ideas and greatest practices for immediate engineering – and find out about an interactive Jupyter Notebooks “sandbox” atmosphere the place we are able to see these ideas utilized to actual examples.
By the top of this lesson we can:
- Clarify what immediate engineering is and why it issues.
- Describe the parts of a immediate and the way they’re used.
- Be taught greatest practices and strategies for immediate engineering.
- Apply realized strategies to actual examples, utilizing an OpenAI endpoint.
Course 5: Creating Superior Prompts
On this chapter, we’ll cowl the next matters:
- Lengthen your data of immediate engineering by making use of totally different strategies to your prompts.
- Configuring your prompts to range the output.
Studying Objectives
After finishing this lesson, you’ll be capable of:
- Apply immediate engineering strategies that enhance the end result of your prompts.
- Carry out prompting that’s both various or deterministic.
Course 6: Constructing Textual content Technology Purposes
On this chapter, you’ll:
- Be taught concerning the openai library and it’s core ideas.
- Construct a textual content technology app utilizing openai.
- Perceive the best way to use ideas like immediate, temperature, and tokens to construct a textual content technology app.
Studying Objectives
On the finish of this lesson, you’ll be capable of:
- Clarify what a textual content technology app is.
- Construct a textual content technology app utilizing openai.
- Configure your app to make use of kind of tokens and likewise change the temperature, for a various output.
Course 7: Constructing Chat Purposes
This lesson covers:
- Methods for effectively constructing and integrating chat functions.
- Learn how to apply customization and fine-tuning to functions.
- Methods and concerns to successfully monitor chat functions.
Studying Objectives
By the top of this lesson, you’ll be capable of:
- Describe concerns for constructing and integrating chat functions into present programs.
- Customise chat functions for particular use-cases.
- Establish key metrics and concerns to observe and keep the standard of AI-powered chat functions successfully.
- Guarantee chat functions leverage AI responsibly.
Course 8: Constructing Search Apps Vector Databases
On this lesson, we’ll cowl:
- Semantic vs Key phrase search.
- What are Textual content Embeddings.
- Making a Textual content Embeddings Index.
- Looking out a Textual content Embeddings Index.
Studying Objectives
After finishing this lesson, it is possible for you to to:
- Inform the distinction between semantic and key phrase search.
- Clarify what Textual content Embeddings are.
- Create an software utilizing Embeddings to seek for knowledge.
Course 9: Constructing Picture Technology Purposes
On this lesson, we’ll cowl:
- Picture technology and why it’s helpful.
- DALL-E and Midjourney, what they’re, and the way they work.
- How you’ll construct a picture technology app.
Studying Objectives
After finishing this lesson, it is possible for you to to:
- Construct a picture technology software.
- Outline boundaries in your software with meta prompts.
- Work with DALL-E and Midjourney.
Course 10: Constructing Low Code AI Purposes
This lesson covers:
- Introduction to Generative AI in Energy Platform
- Introduction to Copilot and the best way to use it
- Utilizing Generative AI to construct apps and flows in Energy Platform
- Understanding the AI Fashions in Energy Platform with AI Builder
Studying Objectives
By the top of this lesson, it is possible for you to to:
- Perceive how Copilot works in Energy Platform.
- Construct a Pupil Task Tracker App for our training startup.
- Construct an Bill Processing Circulation that makes use of AI to extract info from invoices.
- Apply greatest practices when utilizing the Create Textual content with GPT AI Mannequin.
Course 11: Integrating Exterior Purposes with Operate Calling
This lesson will cowl:
- Clarify what’s perform calling and its use instances.
- Making a perform name utilizing Azure OpenAI.
- Learn how to combine a perform name into an software.
Studying Objectives
After finishing this lesson it is possible for you to to:
- Clarify the aim of utilizing perform calling.
- Setup Operate Name utilizing the Azure Open AI Service.
- Design efficient perform calls in your software’s use case.
Course 12: Designing UX for AI Purposes
The lesson will cowl the next areas:
- Introduction to Consumer Expertise and Understanding Consumer Wants
- Designing AI Purposes for Belief and Transparency
- Designing AI Purposes for Collaboration and Suggestions
Studying Objectives
After taking this lesson, you’ll be capable of:
- Perceive the best way to construct AI functions that meet the consumer wants.
- Design AI functions that promote belief and collaboration.
Course 13: Securing Your Generative AI Purposes
This lesson will cowl:
- Safety inside the context of AI programs.
- Widespread dangers and threats to AI programs.
- Strategies and concerns for securing AI programs.
Studying Objectives
After finishing this lesson, you’ll have an understanding of:
- The threats and dangers to AI programs.
- Widespread strategies and practices for securing AI programs.
- How implementing safety testing can stop sudden outcomes and erosion of consumer belief.
Course 14: The Generative AI Software Lifecycle
On this chapter, you’ll:
- Perceive the Paradigm Shift from MLOps to LLMOps
- The LLM Lifecycle
- Lifecycle Tooling
- Lifecycle Metrification and Analysis
Course 15: Retrieval Augmented Technology (RAG) and Vector Databases
On this lesson we’ll cowl the next:
- An introduction to RAG, what it’s and why it’s utilized in AI (synthetic intelligence).
- Understanding what vector databases are and creating one for our software.
- A sensible instance on the best way to combine RAG into an software.
Studying Objectives
After finishing this lesson, it is possible for you to to:
- Clarify the importance of RAG in knowledge retrieval and processing.
- Setup RAG software and floor your knowledge to an LLM
- Efficient integration of RAG and Vector Databases in LLM Purposes.
Course 16: Open Supply Fashions and Hugging Face
Studying Objectives
- Acquire an understanding of open supply Fashions
- Understanding the advantages of working with open supply Fashions
- Exploring the open fashions out there on Hugging Face and the Azure AI Studio
Course 17: AI Brokers
AI Brokers characterize an thrilling improvement in Generative AI, enabling Massive Language Fashions (LLMs) to evolve from assistants into brokers able to taking actions. AI Agent frameworks allow builders to create functions that give LLMs entry to instruments and state administration. These frameworks additionally improve visibility, permitting customers and builders to observe the actions deliberate by LLMs, thereby bettering expertise administration.
The lesson will cowl the next areas:
- Understanding what an AI Agent is – What precisely is an AI Agent?
- Exploring 4 totally different AI Agent Frameworks – What makes them distinctive?
- Making use of these AI Brokers to totally different use instances – When ought to we use AI Brokers?
Studying Objectives
After taking this lesson, you’ll be capable of:
- Clarify what AI Brokers are and the way they can be utilized.
- Have an understanding of the variations between a few of the fashionable AI Agent Frameworks, and the way they differ.
- Perceive how AI Brokers perform as a way to construct functions with them.
Course 18: Advantageous-Tuning LLMs
This lesson introduces the idea of fine-tuning for pre-trained language fashions, explores the advantages and challenges of this strategy, and supplies steerage on when and the best way to use superb tuning to enhance the efficiency of your generative AI fashions.
Studying Objectives
- What is okay tuning for language fashions?
- When, and why, is okay tuning helpful?
- How can I fine-tune a pre-trained mannequin?
- What are the constraints of fine-tuning?
After finishing this lesson, try our Generative AI Studying assortment to proceed leveling up your Generative AI data! Congratsulations!! You’ve gotten accomplished the ultimate lesson from the v2 sequence for this course! Don’t cease studying and constructing. Try the Sources web page for a listing of extra ideas for simply this matter.
Conclusion
The given Microsoft Free Programs on Generative AI marks a major stride in direction of democratizing entry to cutting-edge expertise training. These programs promise to empower learners with the foundational data and sensible abilities wanted to delve into the realm of AI-driven creativity. By offering these Microsoft free programs, Microsoft demonstrates its dedication to fostering innovation and equipping people worldwide with the instruments to harness the potential of Generative AI for numerous functions.
Aside from Microsoft Free Programs, you may unlock your potential with the GenAI Pinnacle Program! Elevate your AI experience by means of revolutionary studying and improvement. Expertise customized 1:1 mentorship with industry-leading Generative AI consultants, dive deep into a sophisticated curriculum that includes over 200 hours of immersive studying, and grasp 26+ cutting-edge GenAI instruments and libraries. Don’t simply be taught AI, pioneer its future with GenAI Pinnacle!
