Key Feature Additions and Changes | Teradata Vantage API Integration - Key Feature Additions and Changes

Key Feature Additions and Changes | Teradata Vantage API Integration - Key Feature Additions and Changes - Teradata Vantage

Teradata Vantage™ - API Integration Guide for Cloud Machine Learning

Deployment

VantageCloud

VantageCore

Edition

Enterprise

IntelliFlex

VMware

Product

Teradata Vantage

Release Number

1.4

Published

September 2023

Language

English (United States)

Last Update

2023-09-28

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The following table lists the key feature additions and changes of Teradata Vantage API Integration.

Date	Release	Feature Additions and Changes
Date	Release	API_Request UDF	teradataml Extension (tdapiclient Package)
September 2023	1.04.00.00	Added support for OpenAI and Azure OpenAI	Static API_Request UDF function extends support for OpenAI and Azure OpenAI
August 2023	1.03.00.00		Added support for the following: TDApiClient Class to fit and deploy Google Vertex AI models within the Vertex AI environment
July 2023	1.02.01.00		Added support for the following: Static API_Request UDF function to call cloud services/API’s outside of AWS, Azure and Google BYOM support for Sagemaker models through TDApiClient.deploy method CSV support through TDApiClient fit method, eliminating data hop for CSV datasets Explicit requirement for the user to allow data format conversion through the client when JSON format is requested - otherwise an error is thrown
February 2023	1.02.00.00	Added support for calling Google Vertex AI endpoint
November 2022	1.01.01.00		Added support for the following: TDApiClient Class to fit and deploy Azure Machine Learning models within the Azure Machine Learning environment TDPredictor Class to predict Azure Machine Learning models on Vantage (or through client) Support for transporting CSV and Parquet files to Azure Blob via TDApiClient fit method Hops through client is not required. BYOMPredictor Class to support the BYOM feature
July 2022	1.01.00.00	Added support for calling Azure Machine Learning endpoint to score model built in Azure Machine Learning
May 2022	1.00.00.00	Support for calling Amazon SageMaker endpoint to score model built in Amazon SageMaker	Support the following: TDApiClient Class to fit and deploy Sagemaker models within the Amazon SageMaker environment TDPredictor Class to predict Sagemaker models on Vantage (or through client) Support for transporting Parquet files to S3 via TDApiClient fit method Hops through client for other file formats on S3