Welcome to BatCat’s documentation!

BatCat is designed to make the data scientist’s life easier when practicing machine learning operations (MLOps) on Amazon Web Services (AWS).

Services of AWS included:

1. AWS Lambda: a serverless, event-driven compute service

2. AWS S3 (Simple Storage Service): provides object storage service

3. Amazon Athena: a serverless, interactive query service on S3

4. Amazon Redshift: a data warehouse product

5. Amazon SageMaker: a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud.

6. Elastic Container Registry (ECR): is a fully managed Docker container registry that makes it easy to store, share, and deploy container images.

7. AWS Step Functions: a low-code, visual workflow service that developers use to build distributed applications, automate IT and business processes, and build data and machine learning pipelines using AWS services.

8. Secrets Manager: is a secure and convenient storage system for API keys, passwords, certificates, and other sensitive data.

Overview

• Philosophy
  • Science, Engineering, and Product
  • Two Approaches: API and Pipeline
• Installation Guide
  • The General Approach
  • The Latest Version
  • For Users in China
• Tutorials
  • Data Science Projects Basics
  • IO Tools
  • Model Deployment on Cloud
• BatCat API
  • Data Science Environment Setup
  • Simple Storage Service (S3)
  • Redshift
  • Athena
  • SageMaker
  • Lambda
  • Elastic Container Registry (ECR)
  • SageMaker Processing
  • Step Functions
• Appendix
  • Machine Learning Operations (MLOps)
  • Identity and Access Management (IAM)
• Contribute to BatCat

Indices

• Index