Amazon S3 Vectors Tutorial | Store & Query Embeddings in AWS (Step by Step) скачать в хорошем качестве

Amazon S3 Vectors Tutorial | Store & Query Embeddings in AWS (Step by Step)

Learn how to use Amazon S3 Vectors step by step! In this video, we’ll cover everything from creating a vector bucket and index in AWS S3 to ingesting and querying vector embeddings with Python. By the end, you’ll understand the complete end-to-end flow of working with S3 Vectors for AI applications. What you’ll learn in this video: What are S3 Vectors and why use them How to create an S3 Vector Bucket Setting up a Vector Index (dimensions, cosine vs Euclidean distance) Writing Python code to ingest embeddings into S3 (using boto3 + Hugging Face) How to query S3 Vectors with filters and return results Understanding Cosine Distance vs Cosine Similarity Best practices and cost optimization tips Use Cases of S3 Vectors: Semantic Search RAG (Retrieval-Augmented Generation) AI Agents Tiered Vector Search Strategies With up to 90% cost savings, Amazon S3 Vectors is a great long-term vector storage option for AI/ML workloads where ultra-low latency is not required. 0:00 – Introduction 0:30 – What are S3 Vectors? 1:39 – Use Cases for S3 Vectors 2:01 – S3 Vector Architecture 2:56 – Creating a Vector Bucket in AWS S3 3:55 – Creating a Vector Index 6:01 – Important Notes on Indexes 6:10 – Ingesting Data into S3 Vectors 8:45 – Querying Data from S3 Vectors 10:42 – Cosine Distance vs Cosine Similarity 11:40 – Conclusion & Recommendations Links: https://github.com/puru2901is/VectorD... https://aws.amazon.com/s3/features/ve... https://docs.aws.amazon.com/AmazonS3/...