How to Build ML Solutions (w/ Python Code Walkthrough)

Get exclusive access to AI resources and project ideas: https://the-data-entrepreneurs.kit.com/shaw This is the 4th video in a series on Full Stack Data Science. Here, I explain why experimentation is critical to the ML lifecycle and walk through the development of a semantic search tool for my YouTube videos. More Resources: 💻 Example Code: https://github.com/ShawhinT/YouTube-Blog/tree/main/full-stack-data-science/data-science 🤖 RAG: https://youtu.be/Ylz779Op9Pw 📚Text Embeddings: https://youtu.be/sNa_uiqSlJo References: [1] https://karpathy.medium.com/software-2-0-a64152b37c35 [2] https://arxiv.org/abs/2012.07919 -- Homepage: https://www.shawhintalebi.com/ Introduction - 0:00 Why ML is Different - 0:39 Role of Experimentation - 3:04 Semantic Search (Design Choices) - 5:09 Example Code: Semantic Search of YT Videos - 8:17 Preview of Final Product - 10:06 Step 1: Experimentation & Evaluation - 11:17 Step 2: Build Video Index - 34:14 Step 3: Build UI - 35:49 What's Next? - 43:43