ML Max is a set of example templates to accelerate the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.
- ML Training Pipeline: This is the process to set up standard training pipelines for machine learning models enabling both immediate experimentation, as well as tracking and retraining models over time.
- ML Inference Pipeline: Deploys a model to be used by the business in production. Currently this is coupled quite closely to the ML training pipeline as there is a lot of overlap.
- Development environment: This module manages the provisioning of resources and manages networking and security, providing the environment for data scientists and engineers to develop solutions.
- Data Management and ETL: This module determines how the machine learning operations interacts with the data stores, both to ingest data for processing, managing feature stores, and for processing and use of output data. A common pattern is to take an extract, or mirror, of the data into S3 on a project basis.