Ravi Kumar Yadav
I’m currently a full-stack machine learning engineer at Walmart E-commerce, where I get to tackle exciting challenges in the world of online retail. Before that, I was a data scientist at Bank of America, building real-time fraud detection models using deep neural networks and big data – talk about high stakes!
My research interests lie in the fascinating areas of graph embedding, neural architecture search, and fast optimization methods for neural networks. I love pushing the boundaries of what’s possible with AI.
But my passion for technology extends beyond my day job. I’m also deeply invested in two side projects:
AI-Powered Vision for IoT: I’m exploring the potential of NVidia Jetson Nano to create innovative machine learning vision applications for the Internet of Things.
ML Design Patterns: I’m developing reusable design patterns to solve common machine learning problems, making AI development more efficient and accessible.
And when I need a break from the digital world, I head to my garden. I’m an avid grower of Cayenne peppers – the hotter, the better!
My journey to AI was paved with diverse experiences. Earlier in my career, I worked on NLP-based automated evaluation of text data, gaining valuable insights into the power of language processing. I hold a master’s degree in computer science from North Carolina State University – Raleigh (graduated in Spring 2016) and a bachelor’s degree in electronics and communication engineering.
Session
In this hands-on tutorial, we’ll walk through building a lightweight, agent-style workflow that takes a user-specified topic and uses retrieval-augmented generation (RAG) to perform deep research, summarize insights, and generate a podcast-style script. We’ll also show how to convert that script into audio using a simple text-to-speech tool.
This is a beginner-friendly, practical workshop that introduces key concepts in agent task design and content orchestration using LLMs.