What are the components of a machine learning system?
Overview - Infrastructure and Tooling
- Google's seminal paper "Machine Learning: The High-Interest Credit Card of Technical Debt" states that if we look at the whole machine learning system, the actual modeling code is very small. There are a lot of other code around it that configure the system, extract the data/features, test the model performance, manage processes/resources, and serve/deploy the model.
- The data component:
- Data Storage - How to store the data?
- Data Workflows - How to process the data?
- Data Labeling - How to label the data?
- Data Versioning - How to version the data?
- The development component:
- Software Engineering - How to choose the proper engineering tools?
- Frameworks - How to choose the right deep learning frameworks?
- Distributed Training - How to train the models in a distributed fashion?
- Resource Management - How to provision and mange distributed GPUs?
- Experiment Management - How to manage and store model experiments?
- Hyper-parameter Tuning - How to tune model hyper-parameters?
- The deployment component
- Continuous Integration and Testing - How to not break things as models are updated?
- Web - How to deploy models to web services?
- Hardware and Mobile - How to deploy models to embedded and mobile systems?
- Interchange - How to deploy models across systems?
- Monitoring - How to monitor model predictions?
- All-In-One: There are solutions that handle all of these components!