Ashwini Mahendiran
Associate Developer - IT @ Flex
Coimbatore, India
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Ashwini Mahendiran is a versatile professional with a Master's degree and over three years of experience in full-stack development and data science. With a proven track record of winning multiple hackathons and research expos, Ms. Mahendiran excels in driving innovation and delivering impactful solutions. Her expertise lies at the intersection of Data, AI, and Software Development, where she leverage cutting-edge technology to address complex challenges. Known for her collaborative approach and strong communication skills, Ms. Mahendiran thrives in dynamic environments, inspiring teams to achieve ambitious goals. Committed to Responsible and Ethical AI, Ms. Mahendiran is passionate about shaping a more inclusive and sustainable future in the tech industry.
Area of Expertise
Topics
AI Agents in LangGraph
you will learn to build an agent from scratch using Python and an LLM, and then you will rebuild it using LangGraph, learning about its components and how to combine them to build flow-based applications.
Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. This will show you how to use agentic search in your applications to provide better data for agents to enhance their output.
Building Trust in the Age of AI: Navigating Responsible AI Ethics and Governance
An intermediate-level session that focuses on the crucial aspects of responsible AI, ethics, and governance. Participants will delve into topics like bias, fairness, transparency, and regulation, gaining a comprehensive understanding of the ethical considerations surrounding AI. By exploring these key issues, the session aims to foster trust in AI systems and equip attendees with insights into implementing responsible AI practices.
Empowering Insights: Developing a Multi-Document RAG Application in Azure
The session will be on how to harness Azure's powerful capabilities to build an innovative and efficient tool for analysis across multiple documents.
1. Introduction to Multi-Document RAG (5 minutes):
Overview of Multi-Document RAG and its significance in research.
Explanation of how RAG models combine retrieval and generation for contextually rich responses.
2.Understanding Azure Services (5 minutes):
Overview of Azure services including Azure Cognitive Search, Azure Functions, and Azure Web Apps.
Explanation of how Azure facilitates scalable, reliable, and secure application development.
3.Data Preparation and Ingestion (5 minutes):
Discussion on preparing documents for ingestion, including formatting and preprocessing.
Demonstration of ingesting multiple documents into Azure Cognitive Search.
4.Building the Retrieval Component (10 minutes):
Explanation of how to leverage Azure Cognitive Search for document retrieval based on user queries.
Walkthrough of implementing search queries and filtering criteria to retrieve relevant documents.
5.Implementing the RAG Model (10 minutes):
Overview of integrating the OpenAI API for RAG model inference.
Demonstration of invoking the RAG model within Azure Functions to generate contextually relevant responses based on retrieved documents.
6.User Interface Development (5 minutes):
Introduction to building a user-friendly interface using Azure Web Apps.
Demonstration of designing a responsive web interface for querying research topics and displaying RAG-generated responses.
7.Scalability and Performance Considerations (3 minutes):
Discussion on optimizing application performance and scalability using Azure's auto-scaling capabilities.
Explanation of best practices for deploying and monitoring the application to ensure optimal performance.
Global Azure 2024-Chennai Sessionize Event
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