{ml}deploy is a Python-based CLI tool and library for quickly containerizing and deploying your machine learning models to cloud services as a REST API.
Code can be local or pulled directly from a GitHub repository. Applications are deployed on cloud services (AWS) within custom generated Docker containers. Using AWS Lambda, AWS SQS, and AWS API Gateway a REST API is exposed for interacting with the deployed application. This can be used for:
- Inference: A deployed model is used to make a prediction given some input data.
- Training: A deployed model framework can be passed different training data, data augmentations, or model architectures for hyperparameter searching.
This is a pre-release work-in-progress. Currently the tool can:
- Setup project folders with configuration files and registry
- Create a Dockerfile and build a Docker image from specified configuration
- Create an AWS ECS cluster via CloudFormation template
The code within this repository is provided as is, for use as dictated by the LICENSE file. Any charges incurred from cloud resources as a result of deploying code is soley the responsibility of the end user. Please be responsible and monitor your resource usage to avoid unwanted or excessive charges.