Introduction

Welcome to the “Semantic Video Search Vector Database” workshop! In this workshop, we will guide you through building a comprehensive video search system using AWS services. You will learn how to ingest, process, and search video data efficiently using AWS tools.

Modules

1. Create OpenSearch Domain and Index with Dataset given

  1. Setting Up S3 for Video Storage

    • Objective: Learn how to use Amazon S3 to store and manage video data.
    • Description: Discover how to set up and configure S3 buckets for video storage, manage data efficiently, and ensure accessibility for processing.

  2. Ingesting Data with ECS Fargate

    • Objective: Understand how to use ECS Fargate for data ingestion and processing.
    • Description: Learn how to deploy and manage containerized applications using ECS Fargate to handle video data ingestion and preprocessing.

  3. Feature Extraction Using SageMaker and ECS

    • Objective: Extract meaningful features from video data using SageMaker.
    • Description: Implement feature extraction algorithms to convert video data into searchable vectors. Learn how to leverage SageMaker’s tools for processing and analyzing video content.

  4. Implementing OpenSearch for Vector Database

    • Objective: Set up and use OpenSearch for storing and querying video vectors.
    • Description: Configure OpenSearch as a vector database to enable fast and efficient search functionality. Learn how to index and retrieve video features for semantic search.

2. Deploy with Terraform

  1. Deploying the Semantic Video Search System
    • Objective: Deploy the complete video search system using Terraform.
    • Description: Use Terraform to automate the deployment of AWS resources, including SageMaker, S3, ECS Fargate, OpenSearch, and Lambda functions. Learn how to set up the entire video search system with ease.

Overview

1. Amazon SageMaker: Amazon SageMaker is a fully managed service that provides tools for building, training, and deploying machine learning models. It offers integrated Jupyter notebooks, automated model tuning, and built-in algorithms, making it easier to develop and manage machine learning workflows.

2. Amazon Simple Storage Service (S3): Amazon S3 is a scalable object storage service that provides high availability and durability for your data. It is commonly used for storing large amounts of data, including video files, with a pay-as-you-go pricing model.

3. Amazon Elastic Container Service (ECS) Fargate: ECS Fargate allows you to run containers without managing the underlying infrastructure. Fargate automates the provisioning and scaling of containerized applications, simplifying data ingestion and processing tasks.

4. OpenSearch: OpenSearch is a search and analytics engine that helps you store, search, and analyze large volumes of data quickly. It provides powerful search capabilities and is ideal for implementing vector databases for semantic search.

5. AWS Lambda: AWS Lambda is a serverless compute service that lets you run code in response to events without provisioning or managing servers. Lambda functions can be used to automate workflows, process data, and integrate various AWS services.

Conclusion

By completing this workshop, you will gain hands-on experience with AWS services to build a scalable and efficient Semantic Video Search Vector Database. You will be equipped with the skills to manage video data, extract features, and implement a powerful search system using AWS technologies.

Join us and start building your own video search solution today!

Keywords: AWS SageMaker, Amazon S3, Amazon ECS Fargate, OpenSearch, AWS Lambda