azure ml
30 TopicsIntroduction to Azure DevOps for Machine Learning
One of the biggest challenges with integrating AI into an application is getting the model deployed into a production environment and keeping it operational/supportable. DevOps for Machine Learning can be streamlined with visibility into training, experiment metrics, and model versions. Azure Machine Learning service allows you to seamlessly integrate with Azure services to provide end-to-end capabilities for the entire Machine Learning lifecycle, making it simpler and faster than ever.7.5KViews5likes3CommentsOperationalize your Prompt Engineering Skills with Azure Prompt Flow
In today’s AI-driven world, prompt engineering is a game-changing skill for developers and professionals alike. With Azure Prompt Flow, you can harness the power of open-source LLMs to solve real-world operational challenges! This article guides you through using Azure’s robust tools to build, deploy, and refine your own LLM apps—from chatbots to data extraction tools and beyond. Whether you're just starting or looking to sharpen your AI expertise, this guide has everything you need to unlock new possibilities with prompt engineering. Dive in and take your tech journey to the next level!1.4KViews5likes3CommentsDeploying a Large Language Model (GPT-2) on Azure Using Power Automate: Step-by-Step Guide
Step-by-step guide on deploying a large language model (GPT-2) to the Azure platform and consuming it using Power Platform (Power Automate, Power Apps) to generate text and give you creative writing ideas.24KViews5likes0CommentsBuilding a digital guide dog for railway passengers with impaired vision
Catching your train on time can be challenging under the best of circumstances. Trains typically only stop for a few minutes, leaving little room for mistakes. For example, at Munich Main station around 240 express trains and 510 regional trains leave from 28 platforms per day. Some trains can also be quite long, up to 346 meters (1,135 ft) for express ICE trains. It is extremely important to quickly find the correct platform and platform section, and then the door closest to a reserved seat needs to be located. This already challenging adventure becomes even more so, if a vision impairment forces a customer to rely exclusively on auditory or tactile feedback. When traveling autonomously, without assistance, it is common practice to walk along the outside of a train, continuously tapping it with a white cane, to discover opened and closed doors (figure 1). While this works in principle, this practice has limitations, both in terms of speed and reliability. We therefore partnered with DB Systel GmbH, the digital partner for all Deutsche Bahn Group companies, to build the Digital Guide Dog. This is a feasibility study based on an AI-powered smartphone application that uses computer vision, auditory and haptic feedback to guide customers to the correct platform section and train car door. In this blog post, we are sharing some of the details and unique challenges that we experienced while the AI model behind this application.Understanding the Difference in Using Different Large Language Models: Step-by-Step Guide
Unlock the secrets of deploying Large Language Models on Azure with our comprehensive guide! Learn step-by-step integration techniques for models like GPT-2, Llama 2, and Dolly v1 in your Web Applications or Power Apps. Explore detailed instructions, ready-made code, and expert tips. Join us for a live session on November 2nd, 2023, to harness the power of AI and Microsoft tools. Become an entrepreneur with Microsoft Founders Hub, offering up to $2,500 OpenAI credits and $1,000 Azure credits. Dive into the world of tech solutions and creative writing ideas today!14KViews3likes1CommentThe Full Guide to Packaging and Deploying ML Models to Production Using Azure: Step-by-Step Guide
Step-by-step guide on How to package and deploy any machine learning model using ONNX to the Azure platform and consume it using Power Platform (Power Automate, Power Apps) to predict house prices.11KViews3likes1Comment