Continuous Learning_Startup & Investment

This is the way how we can serve personalized service by leveraging AI. Data, Model and engineering to combine those two will be important.

1. Overview of Foundation Models and Azure Machine Learning (00:00:00 - 00:05:00)
Greg Buehrer introduces the popularity and application of foundation models like GPT-4 across industries, using Azure Machine Learning. He highlights SynerAI's usage of Azure for analyzing news articles and presents upcoming Azure updates.

2. Deploying Open Source Models and Large Language Models on Azure (00:05:00 - 00:10:00)
Buehrer and Seth Juarez delve into Azure ML's capabilities in generative AI application development, focusing on the Native Model Support and Model Catalog. They explore challenges of Large Language Models (LLMs) through the example of the Eurovision Song Contest and discuss Retrieval Augmented Generation (RAG).

3. Demo: Copilot AI Chatbot for Contoso (00:10:00 - 00:15:00)
The speakers demonstrate a chatbot application built for an outdoor company, Contoso. The chatbot uses Azure Cognitive Search and a RAG model to aid customer service agents by generating accurate and contextually relevant replies.

4. Building Chatbots using LangChain Framework (00:15:00 - 00:20:00)
Daniel Schneider and Seth Juarez discuss how they used the LangChain framework to handle customer intent, product search, and product RAG for their chatbot. They demonstrate how these components work together in the app.

5. Introduction to Prompt Flow in Azure Machine Learning (00:20:00 - 00:25:00)
Prompt Flow, a new Azure ML feature, is introduced. It allows users to run LLM apps in the cloud, experiment with prompts, deploy the app, and monitor its performance. Schneider demonstrates its usage with a chatbot.

6. Improving Classifier Accuracy with One-Shot Pattern (00:25:00 - 00:30:00)
Schneider and Juarez show how to improve the accuracy of the chatbot's customer intent detection by creating a prompt variant using the one-shot pattern. They demonstrate how this change improves the accuracy of the model.

7. Calculating Groundedness of Model Responses (00:30:00 - 00:35:00)
The speakers measure the groundedness (how well responses are based on given context) of their chatbot model's responses using GPT-4. They highlight the importance of responsible groundedness optimization.

8. Deploying Models and Monitoring Performance in Azure Machine Learning (00:35:00 - 00:40:00)
Schneider and Juarez discuss how to deploy models and endpoints in Azure ML and how to use GPT-4 to create vector indices for querying in RAG and Prompt Flow. They also stress the importance of monitoring for maintaining a deployed model.

9. Introduction to the Model Catalog (00:40:00 - End)
The speakers introduce the Model Catalog, a platform where users can fine-tune pre-existing models. They invite users to sign up for access to the platform.

Please note that while this summary condenses the key points, it may omit some detailed discussions, examples, and demonstrations included in the original talk. It would be beneficial to review the full discussion for a comprehensive understanding.

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