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The Layers of AI Platforms

Stanford University_092723A
[Stanford University]

- Overview

AI platforms have layers that allow organizations to deploy machine learning models from a variety of frameworks, languages, platforms, and tools. These layers can be divided into three categories:

  • The data and integration layer: it allows easy access to data from various systems so AI algorithms can be trained. Data should be of good quality so that AI scientists can build data streams without spending time improving data quality. Data management tools provide similar functionality.
  • The experimental layer: it enables data scientists to generate and validate hypotheses. A good experimentation layer automates processes such as feature engineering, feature selection, model selection, model optimization, and model interpretability. AutoML tools also provide similar functionality.
  • The operations and deployment layer: it is where model risk assessments are managed so that the model governance team or compliance team can validate the model. This layer also provides tools for controlling the deployment of models across the enterprise. For example, AI platforms can deploy and scale machine learning models across multiple infrastructure providers. This frees machine learning engineers from having to deal with the details of deploying models on different infrastructures to serve different enterprise applications.


[More to come ...]



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