BatCat, A Cat Looks Like A Bat.
Project description
😸😹😺😻😼😽😾😿🙀🐱
BatCat is designed to help data scientists to practice machine learning operations (MLOps) on Amazon Web Services (AWS).
Services of AWS covered: - AWS Lambda: a serverless, event-driven compute service - AWS S3 (Simple Storage Service): provides object storage service - Amazon Athena: a serverless, interactive query service on S3 - Amazon Redshift: a data warehouse product
Philosophy of BatCat’s MLOps
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:
AWS Lambda: a serverless, event-driven compute service
AWS S3 (Simple Storage Service): provides object storage service
Amazon Athena: a serverless, interactive query service on S3
Amazon Redshift: a data warehouse product
Amazon SageMaker: a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud.
Elastic Container Registry (ECR): is a fully managed Docker container registry that makes it easy to store, share, and deploy container images.
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.
Secrets Manager: is a secure and convenient storage system for API keys, passwords, certificates, and other sensitive data.
Install
pip install batcatStory of the BatCat
The package names after a cat of my friend, Clara.
License
BatCat is licensed under the MIT License. © Contributors, 2024.
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