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 practices MLOps in 3 layers (ASA):
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
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.
1. Algorithm level
Tool: GossipCat, TensorBoard
2. System level
Tool: AWS CloudWatch
AWS CloudWatch provides standard monitoring and operational data with dashboards, which satisfies the requirements of MLOps in system level. Generally, the following operational data are presented in the dashboard:
SageMaker CPU Ultilization
S3 bucket size
- Lambda
invocations
erros
- StepFunction
execution time
execution failed
Cost
Log group
3. Application level
Tool: DataOps
BatCat realizes application level MLOps by monitoring the distributions of data inputs (data source) and data outputs (predictions). As the applicaiton levle MLOps is a part of the whole DataOps, it should algin with the practice of DataOps according to each organziation or company.
Story of the BatCat
The package names after a cat of my friend, Clara.
License
BatCat is licensed under the MIT License. © Contributors, 2022.
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