Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate it with Prompt Flow. By leveraging Azure Machine Learning, and Prompt flow you will establish a workflow for deploying and utilizing custom AI models.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow in Azure AI Studio
Phi-3 is a family of small language models (SLMs) developed by Microsoft that delivers exceptional performance and cost-effectiveness. In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate the custom Phi-3 model with Prompt flow in Azure AI Studio. By leveraging Azure AI / ML Studio, you will establish a workflow for deploying and utilizing custom AI models.Create your own copilot using Azure Prompt flow and Streamlit
Looking to create your own copilot using Azure Prompt flow and Streamlit? This blog will show how generative AI using Azure AI studio prompt flow with Multi-Round Q&A on Your Data chat can make anatomy and physiology education more interactive, engaging, and effective and help students prepare for their healthcare careers.Journey Series for Generative AI Application Architecture - Foundation
At Build last year, Microsoft CTO Kevin Scott proposed Copilot Stack to provide problem-solving ideas for Generative AI applications. Based on the Coplit Stack, community have developed many frameworks in the past year, such as Semantic Kernel, AutoGen, and LangChain. These frameworks are more biased toward front-end applications, and enterprises need a better engineering solution. This series hopes to give you some ideas based on Microsoft Cloud and related frameworks and tools.
Potential Use Cases for Generative AI
Azure’s generative AI, with its Copilot and Custom Copilot modes, offers a transformative approach to various industries, including manufacturing, retail, public sector, and finance. Its ability to automate repetitive tasks, enhance creativity, and solve complex problems optimizes efficiency and productivity. The potential use cases of Azure’s generative AI are vast and continually evolving, demonstrating its versatility and power in addressing industry-specific challenges and enhancing operational efficiency. As more organizations adopt this technology, the future of these sectors looks promising, with increased productivity, improved customer experiences, and innovative solutions. The rise of Azure’s generative AI signifies a new era of intelligent applications that can generate content, insights, and solutions from data, revolutionizing the way industries operate and grow.
