Limitations and Considerations | teradataml open-source machine learning - Limitations and Considerations - Teradata Package for Python

Teradata® Package for Python User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-09
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Product Category
Teradata Vantage
  • Single model generation in VantageCloud Enterprise and VantageCloud Lake might reach memory limits when trained on very large data.
  • split() function of model_selection module returns generator of train and test data when used locally with scikit-learn, but with teradataml OpenSourceML module, it returns generator of train and test as teradataml DataFrames.
  • Functions like predict, transform, and so on, return only labels/predictions in scikit-learn. They return the features data along with labels/predictions in teradataml OpenSourceML.
  • list and delete model in Model Operations are not supported.
    • To list the deployed models, run DataFrame(“opensourceml_models”) or SELECT * FROM opensourceml_models.
    • To delete the models, run SQL query to delete the rows from the table “opensourceml_models”.
  • Classes that take generators, iterators, functions, and callable objects as arguments are not supported.
  • feature_extraction module is not supported.

See attached Supportability Matrix for teradataml OpenSourceML - scikit-learn classes and functions support.