Why AI in JS

Most developers would agree that getting started with AI development can be intimidating. AI with JavaScript provides an approachable entry point, allowing devs to dip into AI without needing to master complex math or frameworks. Attend this session and see code demos running Tensorflow.js, Mediapipe, Transformers.js, Chrome Built-in AI - all running inside of the browser. Why? To ditch python for AI. Heck, ditch servers for AI too! AI in JS brings Zero installs, Client privacy, and Cross-platform support.

Developing Spidey Sense : Anomaly Detection for apps

Anomaly detection is the process of identifying unexpected items or events in data sets. It’s about detecting the deviation from expected pattern of a dataset. It’s like having “spidey senses” for your apps that can detect when there’s danger or something is not right. Attend this session and learn about using anomaly detection in ML.NET, Azure Stream Analytics and Cognitive Services API; become a superhero and save the day.

Agentic AI Design Patterns

As artificial intelligence continues to evolve, we're witnessing a paradigm shift in how AI systems are structured and deployed. Instead of monolithic models, modern AI architectures increasingly rely on multiple specialized agents working together to accomplish complex tasks. In this presentation, we’ll explore Agentic AI design patterns by drawing a playful comparison to the minions. Just like these quirky characters, AI Agents autonomously perform tasks, follow instructions, and work together toward a common goal. Through this lighthearted analogy, we’ll see how minions' chaotic teamwork mirrors the structured collaboration of AI agents, helping you grasp these advanced concepts in a fun and relatable way.

Prompt Potions & AI Agent Alchemy

Think of prompts in a language model as magic spells: you say what you want, and like magic, it gives you an answer. Imagine AI agents as wizards, consulting their spell books (in this case, a special kind of AI called Generative Pre-trained Transformer, or GPT) to decide which spells (actions) to cast and when. In this session, we'll show you how to become a wizard yourself by teaching you to craft your own AI agents. Using tools like OpenAI, LangChainJS, and some clever trickery with words, you'll learn the art of prompt engineering. Ready to unlock the magic? Join us and say 'Alohomora!' to the world of AI."

Predicting the future (of equipment) using ML.NET

Predicting the future is not easy! Forecasting peak operating conditions and avoiding unexpected equipment breakdown is possible, that’s the goal of Predictive Maintenance. Attend this session and learn how to train a machine learning model in Visual Studio with ML.NET Model Builder, and build an app that uses the model. Fair warning - I cannot predict how live demos would go. I will do my best.

AI Coaching Playbook: Strategies for Multi-Agent Systems

With the advancement in Generative AI and Large Language Models, there's a fundamental shift on how we develop applications. Engineers are not just building blueprints and code instructions, we are being called to be good AI coaches. Expert Coaches that can guide multiple AI agents to discover, learn, collaborate and/or compete with each other. Come and join the presentation to learn practical strategies to effectively coach AI systems, ensuring they perform at their best and allowing us to find the best version FTW!

Managing IoT Devices in Azure; the simpler way

Collecting telemetry data from devices is a common requirement today for any IoT solution. In this session, we will learn about Azure IoT Central and how it reduces the burden and cost of developing, managing, and maintaining IoT applications. Journey with me as we will walk through different features of IoT Central: connect devices, monitor, configure rules, and integrate with other services. Learn how to customize IoT central to fit the needs.

Making neural networks portable with ONNX

The world of machine learning frameworks is complex. What if we can use the lightest framework for inferencing on edge devices? That’s the idea behind ONNX format. Attend this session and find out how to train models using the framework of your choice, save or convert models into ONNX, and deploy to cloud and edge using a high-performance runtime in python, C# or javascript.

Leverage Power of Machine Learning with ONNX

Have you ever wanted to make your apps “smarter”? This session will cover what every ML/AI developer should know about Open Neural Network Exchange (ONNX) . Why it’s important and how it can reduce friction in incorporating machine learning models to your apps. We will show how to train models using the framework of your choice, save or convert models into ONNX, and deploy to cloud and edge using a high-performance runtime.

Shaken, Not Stirred: Prompt Engineering & Semantic Kernel Mixology

Prompts play a crucial role in communicating and directing the behavior of Large Language Models (LLMs). It's like giving the model a question or topic to chat about, and it does its best to provide a relevant answer or information. Semantic Kernel is like a mixologist who looks at available ingredients and crafts new cocktail recipes. Semantic Kernel looks at different plugins, AI Prompts, and memory stores to create an execution plan. Attend this presentation to learn how Semantic Kernel can be used to build a copilot that’s creative enough to suggest new cocktails. Cheers!

Prompts play a crucial role in communicating and directing the behavior of Large Language Models (LLMs). Semantic Kernel (SK) looks at different plugins, prompts, and memory stores to create a plan. Attend this presentation to learn SK in .NET and build an agent to suggest new cocktails. Cheers!

Build Intelligent applications with ML.NET and Windows Machine Learning

Need to integrate trained machine learning models into Windows apps? In this session, we will look into how Windows Machine Learning can transform applications with the power of artificial intelligence and run it on the devices by taking full advantage of hardware acceleration. We will walk thru and learn how to train a model with ML.NET Model Builder and inference with Windows Machine Learning.

Context-aware Agents into Web Apps

In a galaxy not so far away, every user deserves a trusty sidekick like R2-D2—a copilot that truly understands their needs. What if our web apps could offer the same seamless interaction? In this presentation, we'll explore how to embed AI Agents into web apps to read contextual data, such as user inputs, form elements, and navigation patterns. These copilots can make your app smarter and responding to context in real-time. May the AI Be with you!

What you will learn:

- Embed a contextual chat feature: Engage users by answering queries and performing tasks based on conversational inputs.
- Enable action-driven assistance: Allow the copilot to autonomously perform tasks, boosting interactivity and efficiency.
- Offer dynamic content recommendations

AI Agents on Demand

What do Deadpool, Ethan Hunt (Mission: Impossible) & Sherlock Holmes have in common? They’re on-demand agents — hired for a mission, bringing their skills, tools, and vanishing after the job’s done. AI Agents work the same way: purpose-built, on-demand, and mission-ready. Your mission, should you choose to accept, is to learn how to build scalable AI agents with Azure AI Agent Service in JavaScript — combining reasoning, tools & automation for next-gen cloud solutions. This message will self-destruct in 5 seconds.