Level up your Python + AI skills with our complete series
We've just wrapped up our live series on Python + AI, a comprehensive nine-part journey diving deep into how to use generative AI models from Python. The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To help you apply what you've learned, all of our code examples work with GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. If you're a Spanish speaker, check out the Spanish version of the series. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across many types of content. In this session, we explore different vector embedding models, such as the OpenAI text-embedding-3 series, through both visualizations and Python code. We compare distance metrics, use quantization to reduce vector size, and experiment with multimodal embedding models. Slides for this session Code repository with examples: vector-embedding-demos Python + AI: Retrieval Augmented Generation 📺 Watch recording In our third session, we explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that provides context to the LLM, enabling it to deliver well-grounded answers for a particular domain. The RAG approach works with many types of data sources, including CSVs, webpages, documents, and databases. In this session, we walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vision models 📺 Watch recording Our fourth session is all about vision models! Vision models are LLMs that can accept both text and images, such as GPT-4o and GPT-4o mini. You can use these models for image captioning, data extraction, question answering, classification, and more! We use Python to send images to vision models, build a basic chat-with-images app, and create a multimodal search engine. Slides for this session Code repository with examples: openai-chat-vision-quickstart Python + AI: Structured outputs 📺 Watch recording In our fifth session, we discover how to get LLMs to output structured responses that adhere to a schema. In Python, all you need to do is define a Pydantic BaseModel to get validated output that perfectly meets your needs. We focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples demonstrate the many ways you can use structured responses, such as entity extraction, classification, and agentic workflows. Slides for this session Code repository with examples: python-openai-demos Python + AI: Quality and safety 📺 Watch recording This session covers a crucial topic: how to use AI safely and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. We focus on Azure tools that make it easier to deploy safe AI systems into production. We demonstrate how to configure the Azure AI Content Safety system when working with Azure AI models and how to handle errors in Python code. Then we use the Azure AI Evaluation SDK to evaluate the safety and quality of output from your LLM. Slides for this session Code repository with examples: ai-quality-safety-demos Python + AI: Tool calling 📺 Watch recording In the final part of the series, we focus on the technologies needed to build AI agents, starting with the foundation: tool calling (also known as function calling). We define tool call specifications using both JSON schema and Python function definitions, then send these definitions to the LLM. We demonstrate how to properly handle tool call responses from LLMs, enable parallel tool calling, and iterate over multiple tool calls. Understanding tool calling is absolutely essential before diving into agents, so don't skip over this foundational session. Slides for this session Code repository with examples: python-openai-demos Python + AI: Agents 📺 Watch recording In the penultimate session, we build AI agents! We use Python AI agent frameworks such as the new agent-framework from Microsoft and the popular LangGraph framework. Our agents start simple and then increase in complexity, demonstrating different architectures such as multiple tools, supervisor patterns, graphs, and human-in-the-loop workflows. Slides for this session Code repository with examples: python-ai-agent-frameworks-demos Python + AI: Model Context Protocol 📺 Watch recording In the final session, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible. We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like LangGraph and Microsoft agent-framework to MCP servers. With great power comes great responsibility, so we briefly discuss the security risks that come with MCP, both as a user and as a developer. Slides for this session Code repository with examples: python-mcp-demoModel Mondays S2:E4 Understanding AI Developer Experiences with Leo Yao
This week in Model Mondays, we put the spotlight on the AI Toolkit for Visual Studio Code - and explore the tools and workflows that make building generative AI apps and agents easier for developers. Read on for my recap. This post was generated with AI help and human revision & review. To learn more about our motivation and workflows, please refer to this document in our website. About Model Mondays Model Mondays is a weekly series designed to help you grow your Azure AI Foundry Model IQ step by step. Each week includes: 5-Minute Highlights – Quick news and updates about Azure AI models and tools on Monday 15-Minute Spotlight – Deep dive into a key model, protocol, or feature on Monday 30-Minute AMA on Friday – Live Q&A with subject matter experts from the Monday livestream If you're looking to grow your skills with the latest in AI model development, this series is a great place to begin. Useful links: Register for upcoming livestreams Watch past episodes Join the AMA on AI Developer Experiences Visit the Model Mondays forum Spotlight On: AI Developer Experiences 1. What is this topic and why is it important? AI Developer Experiences focus on making the process of building, testing, and deploying AI models as efficient as possible. With the right tools—such as the AI Toolkit and Azure AI Foundry extensions for Visual Studio Code—developers can eliminate unnecessary friction and focus on innovation. This is essential for accelerating the real-world impact of generative AI. 2. What is one key takeaway from the episode? The integration of Azure AI Foundry with Visual Studio Code allows developers to manage models, run experiments, and deploy applications directly from their preferred development environment. This unified workflow enhances productivity and simplifies the AI development lifecycle. 3. How can I get started? Here are a few resources to explore: Install the AI Toolkit for VS Code Explore Azure AI Foundry Documentation Join the Microsoft Tech Community to follow and contribute to discussions 4. What’s New in Azure AI Foundry? Azure AI Foundry continues to evolve to meet developer needs with more power, flexibility, and productivity. Here are some of the latest updates highlighted in this week’s episode: AI Toolkit for Visual Studio Code Now with deeper integration, allowing developers to manage models, run experiments, and deploy applications directly within their editor—streamlining the entire workflow. Prompt Shields Enhanced security capabilities designed to protect generative AI applications from prompt injection and unsafe content, improving reliability in production environments. Model Router A new intelligent routing system that dynamically directs model requests to the most suitable model available—enhancing performance and efficiency at scale. Expanded Model Catalog The catalog now includes more open-source and proprietary models, featuring the latest from Hugging Face, OpenAI, and other leading providers. Improved Documentation and Sample Projects Newly added guides and ready-to-use examples to help developers get started faster, understand workflows, and build confidently. My A-Ha Moment Before watching this episode, setting up an AI development environment always felt like a challenge. There were so many moving parts—configurations, integrations, and dependencies—that it was hard to know where to begin. Seeing the AI Toolkit in action inside Visual Studio Code changed everything for me. It was a realization moment: “That’s it? I can explore models, test prompts, and deploy apps—without ever leaving my editor?” This episode made it clear that building with AI doesn’t have to be complex or intimidating. With the right tools, experimentation becomes faster and far more enjoyable. Now, I’m genuinely excited to build, test, and explore new generative AI solutions because the process finally feels accessible. Coming Up Next Week In the next episode, we’ll be exploring Fine-Tuning and Distillation with Dave Voutila. This session will focus on how to adapt Azure OpenAI models to your unique use cases and apply best practices for efficient knowledge transfer. Register here to reserve your spot and be part of the conversation. Join the Community Building in AI is better when we do it together. That’s why the Azure AI Developer Community exists—to support your journey and provide resources every step of the way. Join the Discord for real-time discussions, events, and peer learning Explore the Forum to catch up on AMAs, ask questions, and connect with other developers About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador passionate about cloud technologies and artificial intelligence. I enjoy learning, building, and helping others grow in tech. Connect with me: LinkedIn GitHub Dev.to Microsoft Tech CommunityHow Azure Storage Powers AI Workloads: Behind the Scenes with OpenAI, Blobfuse & More
In the latest episode of E2E: 10-minute Drill, I sat down with Vamshi from the Azure Storage team to explore how Azure Blob Storage is fueling the AI revolution. From training massive foundation models like ChatGPT to enabling enterprise-grade AI solutions. Whether you're building your own LLM, fine-tuning models with proprietary data, or just curious about how Microsoft supports OpenAI’s infrastructure, this episode is packed with insights. 🎥 Watch the Full Episode 👉Watch on YouTube 🔍 Key Highlights Azure Blob Storage is the backbone of AI workloads, storing everything from training data to user-generated content in apps like ChatGPT and DALL·E. Microsoft’s collaboration with OpenAI has led to innovations like Azure Scaled Accounts and Blobfuse2, now available to all Azure customers. Enterprises can now securely bring their own data to Azure AI services, with enhanced access control and performance at exabyte scale. 📂 Documentation & Resources 🚀 Azure Blob Storage Overview - https://learn.microsoft.com/azure/storage/blobs/ 📝 Blobfuse2 (Linux FUSE Adapter for Azure Blob Storage) - https://learn.microsoft.com/azure/storage/blobs/blobfuse2-introduction 🧠 Azure OpenAI Service - https://learn.microsoft.com/azure/cognitive-services/openai/overview 🔐 Azure Role-Based Access Control (RBAC) - https://learn.microsoft.com/azure/role-based-access-control/overview 💬 Why It Matters As AI becomes a core workload for infrastructure teams, understanding how to scale, secure, and optimize your data pipelines is critical. This episode offers a behind-the-scenes look at how Microsoft is enabling developers and enterprises to build the next generation of intelligent applications—using the same tools that power OpenAI. 📣 Stay Connected Subscribe to the ITOpsTalk YouTube Channel and follow the E2E:10-minute Drill series for more conversations on cloud, AI, and innovation. And, As always if you have any questions or comments, please leave them below. I'll make sure we get back to you. Cheers!! PierreBuilding intelligent agents with Copilot Studio agent builder
Imagine a workspace where productivity flows smoothly and improves continuously. In the era of generative AI, organizations and employees are experiencing unprecedented enhancements in efficiency and creativity. Generative AI systems can automate routine tasks, freeing up valuable time for employees to focus on more strategic initiatives. These intelligent assistants analyze vast amounts of data to provide actionable insights, helping teams make informed decisions swiftly. Furthermore, they assist in content creation, data analysis, and even customer interactions, ensuring that businesses can operate more effectively and adapt to the ever-evolving market demands. This technological advancement not only optimizes workflows but also empowers employees to innovate and achieve their full potential, ultimately driving success and growth within the organization. Microsoft 365 Copilot is your AI assistant, and Copilot Studio is where you easily create and manage agents – think of these as the new apps for an AI-powered world. Agents connect to your organization’s knowledge and data sources, to automate and execute business processes, working alongside or on behalf of a person, team, or organization. Agents range from simple, prompt-and-response agents to more advanced, fully autonomous agents. We envision a workplace where every organization will have agents, working on behalf of individuals, teams, or functions. What is Copilot Studio agent builder? The Copilot Studio agent builder is a low-code platform that enables users to build custom agents directly within Microsoft 365 Copilot Business Chat (BizChat). This powerful tool allows users to create intelligent agents that can automate tasks, provide valuable insights, and enhance overall productivity. The agent builder offers an intuitive user interface, with seamless integration with various data sources, and quick deployment options. This tool allows you to describe your agent, create, test, and deploy it, making it accessible even to individuals with minimal technical expertise. Key Features Ease of Use: The agent builder is designed for users with minimal coding experience. With its intuitive interface, you can create agents by simply describing the type of agent you want to build and configuring its settings. Customization: You can customize your agent by editing its name, description, and instructions. Additionally, you can add SharePoint sites and documents that your agent should reference, as well as starter prompts to guide its interactions. Integration: The agents built with Copilot Studio can be integrated into various Microsoft 365 applications, including Microsoft Teams, BizChat, and more. This ensures that your agents are accessible wherever you need them. Scalability: The platform supports the creation of knowledge-only agents, with additional capabilities such as image generation coming soon. This allows you to scale your agents' functionality as your business needs evolve. How to get started To help showcase what’s possible with agents created using Copilot Studio agent builder, we have made available agents, pre-built for you, like Career Coach, Prompt Coach and Writing Coach. You can use them as a starting point in creating agents, connect to your knowledge sources and improve your business workflows. Or you can create your own agent. To get started with agent builder. Access BizChat: Go to BizChat experience in Teams, Microsoft365.com/Copilot, or other Microsoft 365 apps. Create an agent: Select "Create an agent" and describe the type of agent you want to build. Add any further descriptions and configure its settings. Customize your agent: Edit the agent's name, description, and instructions. Add relevant SharePoint sites, documents, and prompt starters. Deploy your agent: Once your agent is ready, select "Create" and then "Share" to deploy it within BizChat in your organization. Benefits of using Copilot Studio agent builder Enhanced efficiency: Automate repetitive tasks and free up valuable time for your team to focus on strategic activities. Improved customer engagement: Provide timely and accurate responses to customer inquiries, enhancing their overall experience. Streamlined processes: Integrate agents into your existing workflows to streamline operations and improve productivity. Get started building agents today! The Copilot Studio agent builder is a game-changer for businesses looking to leverage the power of intelligent agents. By providing a low-code platform for creating custom agents, it empowers users to enhance efficiency, improve customer engagement, and streamline processes. Whether you're looking to automate tasks or provide valuable insights, the agent builder is a tool worth exploring. Whether a user, developer, or IT professional, with the ability to build agents in Copilot Studio you can build an agent and customize it for your business’s specific needs and processes and get the most of AI. These features are generally available today. Explore the comprehensive documentation and user guide to get started.DevOps in the era of Generative AI: Foundations of LLMOps
Spotlight on AI in your DevOps Lifecycle Explore the transformative power of artificial intelligence in DevOps with our comprehensive series, "Spotlight on AI in Your DevOps Lifecycle." This series delves into the integration of AI into every stage of the DevOps process, providing invaluable insights and practical guidance. Whether you're a seasoned professional or new to the field, these episodes will equip you with the knowledge to leverage AI effectively in your development and operations lifecycle.
