RAG (Retrieval Augmented Generation) is the most popular approach used to get LLMs to answer user questions grounded in a domain. How can you be sure that the answers are accurate, clear, and well formatted? Evaluation! In this session, we'll show you how to use Azure AI Studio and the Promptflow SDK to generate synthetic data and run bulk evaluations on your RAG app. Learn about different GPT metrics like groundedness and fluency, and consider other ways you can measure the quality of your RAG app answers.

Presented by Nitya Narasimhan, AI Advocate, and Pamela Fox, Python Advocate

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

**📌 Check out the RAGHack 2024 series here! **

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most popular approach used to get LLMs to answer user questions grounded in a domain. How can you be sure that the answers are accurate, clear, and well formatted? Evaluation! In this session, we'll show you how to use Azure AI Studio and the Promptflow SDK to generate synthetic data and run bulk evaluations on your RAG app. Learn about different GPT metrics like groundedness and fluency, and consider other ways you can measure the quality of your RAG app answers.

Presented by Nitya Narasimhan, AI Advocate, and Pamela Fox, Python Advocate

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

**📌 Check out the RAGHack 2024 series here! **

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG (Retrieval Augmented Generation) is the most common approach used to get LLMs to answer questions grounded in a particular domain's data. Learn how to build a RAG-based custom copilot end-to-end using Azure AI Studio, code-first. We'll walk through "Contoso Chat", a retail copilot scenario with product and customer data. We'll explore prompt engineering using prompty assets, orchestration with promptflow flex-flows and automated provisioning and deployment with azd. You'll learn how to build & test your copilot locally (in VS Code), then deploy & test it in production on Azure.

Presented by Leah Bar-On Simmons (Azure AI PM) and Nitya Narasimhan (Azure AI Advocate)

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

• Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

Learn how to debug an AI model using the Responsible AI dashboard in Azure Machine Learning studio to ensure it performs responsibly and is less harmful. Create a responsible AI dashboard. Identify where the model has errors. Discover data over or under representation to mitigate biases. Understand what drives a model outcome with explainable and interpretability. Mitigate issues to meet compliance regulation requirements.

Learning objectives - Create a responsible AI dashboard. - Identify where the model has errors. - Discover data over or under representation to mitigate biases. - Understand what drives a model outcome with explainable and interpretability. - Mitigate issues to meet compliance regulation requirements.

Presenters: Nitya Narasimhan \|\| Senior Cloud Advocate - Microsoft - LinkedIn: https://www.linkedin.com/in/nityan/ Ruth Yakubu \|\| Cloud Advocate - Microsoft - Twitter: https://twitter.com/ruthieyakubu - LinkedIn: https://www.linkedin.com/in/ruthyakubu/

Moderators: Cynthia Zanoni \|\| Cloud Advocate - Microsoft - LinkedIn: https://www.linkedin.com/in/cynthiazanoni/

📌Full series information

📌More info here

Learn how to debug an AI model using the Responsible AI dashboard in Azure Machine Learning studio to ensure it performs responsibly and is less harmful. Create a responsible AI dashboard. Identify where the model has errors. Discover data over or under representation to mitigate biases. Understand what drives a model outcome with explainable and interpretability. Mitigate issues to meet compliance regulation requirements.

Learning objectives - Create a responsible AI dashboard. - Identify where the model has errors. - Discover data over or under representation to mitigate biases. - Understand what drives a model outcome with explainable and interpretability. - Mitigate issues to meet compliance regulation requirements.

Presenters: Nitya Narasimhan \|\| Senior Cloud Advocate - Microsoft - LinkedIn: https://www.linkedin.com/in/nityan/ Ruth Yakubu \|\| Cloud Advocate - Microsoft - Twitter: https://twitter.com/ruthieyakubu - LinkedIn: https://www.linkedin.com/in/ruthyakubu/

Moderators: Cynthia Zanoni \|\| Cloud Advocate - Microsoft - LinkedIn: https://www.linkedin.com/in/cynthiazanoni/

📌Full series information

📌More info here

** Important RSVP here (Due to room capacity and building security, you must pre-register at the link for admission)

Description: Welcome to our in-person AI meetup in New York. Join us for deep dive tech talks on AI, GenAI, LLMs and ML, hands-on workshops, food/drink, networking with speakers and fellow developers.

Tech Talk: End-to-End Development of Generative AI applications on Azure Speaker: Nitya Narasimhan (Microsoft) Abstract: In this talk we’ll introduce the core concepts for building a “copilot” application on Azure AI from prompt engineering to LLM Ops – using the Contoso Chat application sample as a reference. And we’ll explore the Azure AI Studio (preview) platform from a code-first perspective to understand how you can streamline your development from model exploration to endpoint deployment, with a unified platform and workflow.

Tech Talk: Train & Debug your ML Models For Responsible AI on Azure Speaker: Ruth Yakubu (Microsoft) Abstract: In this talk you will learn to train an AI model using the Azure Machine Learning Studio, then use its built-in Responsible AI Dashboard capability to debug your model for performance, fairness and responsible AI usage.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsor. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in New York or 300K+ worldwide.

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