Machine Learning for Advanced AI Search

Explore the fundamentals of machine learning applied to advanced AI search systems in this 27-minute conference talk from Conf42 ML 2025. Begin with an introduction to generative AI and foundation models, learning how these systems process inputs and outputs while understanding customization techniques. Dive into the critical role of vectors in machine learning, discovering how vector embeddings work and why they're essential for modern AI applications. Examine vector stores and databases, with particular focus on AWS vector data stores and their practical implementations. Master PostgreSQL and PG Vector for vector database management, exploring their key features, use cases, and querying capabilities. Gain hands-on insights into building and optimizing search systems that leverage machine learning for enhanced performance and accuracy.

Syllabus

00:00 Introduction to Machine Learning for Advanced AI Search
00:32 Understanding Generative AI
01:47 Foundation Models Explained
03:13 Inputs and Outputs of Foundation Models
05:15 Customizing Foundation Models
08:57 Introduction to Vectors in Machine Learning
10:04 Vector Embeddings and Their Importance
11:23 Vector Stores and Databases
12:59 Exploring AWS Vector Data Stores
16:10 PostgreSQL and PG Vector for Vector Databases
19:01 PG Vector: Features and Use Cases
20:18 Querying with PG Vector
25:07 Summary and Conclusion