Machine Learning Pipes on the C3 Agentic AI Platform

A pipe is one of the building blocks for a pipeline, that is either a pipeline itself or an atomic pipe. It corresponds to a contained unit of machine learning estimator or transformer.

In the C3 Agentic AI Platform, MlPipes are used as building blocks, which can be used to define and implement custom pipes in Python using external frameworks.

For details on creating pipes MlPipes, see one of the following tutorials:

• Author Machine Learning Pipelines - MlPipeline Tutorial
• Author Custom Machine Learning Pipes in Native Python - MlLambdaPipe Tutorial
• Author Custom Machine Learning Pipes in Native Python - MlDynamicPipe Tutorial
• Hyperparameter Optimization at Scale - HPO Tutorial
• Interpretability Deep Dive - ShapInterpreter Authoring and Execution Tutorial

The following Python libraries are supported:

• gluonts
• huggingface
• keras
• sklearn
• sktime
• spacy

See also Overview of Pandas APIs.

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See also

• Use the C3 AI Feature Store Overview
• Create Features Using C3 Metrics or Metric Expressions
• Model Deployment Overview
• Model Registry on the C3 Agentic AI Platform
• Approve Models in the Model Registry