AI-900: Microsoft Azure AI Fundamentals Study Guide
This comprehensive study guide provides a thorough overview of the topics covered in the Microsoft Azure AI Fundamentals (AI-900) exam, including Artificial Intelligence workloads, fundamental principles of machine learning, computer vision and natural language processing workloads. Learn about the exam's intended audience, how to earn the certification, and the skills measured as of April 2022. Discover the important considerations for responsible AI, the capabilities of Azure Machine Learning Studio and more. Get ready to demonstrate your knowledge of AI and ML concepts and related Microsoft Azure services with this helpful study guide.Starting your Kaggle challenge using Azure Machine Learning Services
One of the main advantages of Azure ML is the ability to do hyperparameter optimization by scheduling experiments. So have you tried this this with dataset hosted on Kaggle? Kaggle has over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time. Kaggle does offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. However, there are times when you want to build your experiment using Azure and an AzureML workspace in the azure portal.Microsoft Learn AI Skills Challenge
Join Microsoft's AI Skills Challenge 2023 to enhance your technical expertise in Artificial Intelligence. Register now to access exclusive resources, hands-on labs, and interactive learning sessions. Boost your knowledge in generative AI, machine learning, cognitive services, natural language processing, and computer vision to stay ahead in the ever-evolving world of AI.Build your first ML-Model with ML.NET Model Builder
Excited to dive into machine learning in .NET? With the aid of tools like ML.NET Model Builder and Visual Studio, it's a breeze. Here's a preview of the steps you'll take: 1. Download Visual Studio 2022 with .NET desktop development and ML.NET Model Builder. 2. Create a .NET console app named myMLApp. 3. Add a machine learning model named SentimentModel.mbconfig. 4. Choose the Data classification scenario. 5. Select Local (CPU) as the training environment. 6. Prepare and import your data. 7. Train the model. 8. Evaluate its performance. 9. Consume the model using provided code. 10. Run and debug to observe the results. Now you're all set to leverage ML.NET's prowess for predictive models in your .NET apps!Tiny But Mighty: Unleashing the Power of Small Language Models 🚀
While Large Language Models (LLMs) like GPT-4 dominate headlines with their extensive capabilities, they often come at the cost of high computational requirements and complexity. For developers and organizations looking to implement AI solutions on edge devices or with limited resources, Small Language Models (SLMs) are emerging as a practical alternative. SLMs are not just "smaller" versions of their larger counterparts—they're designed to be faster, more efficient, and adaptable for specific tasks. With fewer parameters and lower computational needs, SLMs open the door to deploying AI on mobile devices, IoT systems, and edge environments without compromising performance. What You Stand to Learn 🧠 Introduction to Microsoft's AI Ecosystem Discover Microsoft's end-to-end AI development tools, from Azure AI Services to ONNX Runtime, enabling efficient and secure deployment of AI models across cloud and edge environments. The Advantages of SLMs over LLMs SLMs are game-changers for edge AI applications, providing faster training and inference times, reduced energy costs, and scalability across diverse devices. Hands-On with Phi-3 and ONNX Runtime Experience live demonstrations of SLMs in action with tools like Phi-3 and ONNX Runtime, showcasing how to fine-tune and deploy models on mobile devices, IoT, and hybrid cloud environments. Responsible AI Practices Understand how to safeguard your AI applications with Microsoft's Responsible AI toolkit, ensuring ethical and trustworthy deployments. Watch the Full Session 👨💻 📅 Date: December 12, 2024 ⏰ Time: 4 PM GMT | 5 PM CEST | 8 AM PT | 11 AM ET | 7 PM EAT A session packed with live demos, practical examples, and Q&A opportunities. Register NOW | Events | Microsoft Reactor Agenda 🔍 Introduction (5 min) A brief overview of the session and its focus on SLMs and LLMs. Microsoft AI Tooling (5 min) Explore the latest tools like Azure AI Services, Azure Machine Learning, and Responsible AI Tooling. How to Choose the Right Model (10 min) Key considerations such as performance, customizability, and ethical implications. Comparing SLMs vs LLMs (10 min) The strengths, weaknesses, and best use cases for both Small and Large Language Models. Deploying Models at the Edge (10 min) Insights into optimizing AI for mobile, IoT, and edge devices. Q&A Addressing participant questions about AI development and deployment.Responsible Synthetic Data Creation for Fine-Tuning with RAFT Distillation
This blog will explore the process of crafting responsible synthetic data, evaluating it, and using it for fine-tuning models. We’ll also dive intoAzure AI’sRAFT distillation recipe, a novel approach to generating synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.
