Using Teradata API Integration for Cloud Machine Learning
Teradata Vantage is more than a high performance, scalable data platform, it is also an open analytics platform, offering a library of built-in analytic functions and extensible frameworks to enable our customers to integrate with their full ecosystem of analytics tools and platforms.
| Platform | Description |
|---|---|
| Azure Machine Learning | Overview Teradata introduces integration with Azure Machine Learning, which offers services to train machine learning models and host models for online and batch scoring. With this integration, customers can connect to online Azure Machine Learning endpoint and score using Vantage data. |
| Components This integration includes teradataml Extension Library (tdapiclient) and Vantage in-database function (API_Request). The Python client package allows data scientists and developers to use their Python development environment to connect to Vantage and call the in-database function with a Python-style function call instead of SQL, for scoring and inference of Vantage data with Azure Machine Learning endpoints. |
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| Google Vertex AI | Overview Teradata introduces integration with Google Vertex AI, which offers services to train machine learning models and host models for online and batch scoring. With this integration, customers can connect to online Google Vertex AI endpoint and score using Vantage data. |
| Components This integration includes teradataml Extension Library (tdapiclient) and Vantage in-database function (API_Request). The Python client package allows data scientists and developers to use their Python development environment to connect to Vantage and call the in-database function with a Python-style function call instead of SQL, for scoring and inference of Vantage data with Google Cloud Vertex AI endpoints. |
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| OpenAI and Azure OpenAI | Overview OpenAI and Azure OpenAI provide multiple APIs for their hosted models. Teradata introduces integration with the embedding API, which can be used in the following type of applications: Classification, Search, Recommendations, and Anomaly detection. |
| Components This integration includes teradataml function TDApiClient.API_Request and Vantage in-database function (API_Request). The Teradata Python client package function TDApiClient.API_Request is a static help function that is to invoke the in-database function API_Request. |
Why Would I Use this Content?
| Platform | Description |
|---|---|
| Azure Machine Learning | Teradata API Integration with Azure Machine Learning gives customers tools to operationalize analytic workflows on their Vantage platform, connecting their integrated enterprise data with Azure analytics using ‘real-time’ queries and putting analytic results in the hands of their business and analytic teams to bring more insights and analytic value to customers data, and drive outcomes. |
| Google Vertex AI | Teradata API Integration with Google Vertex AI gives customers tools to operationalize analytic workflows on their Vantage platform, connecting their integrated enterprise data with Google Vertex AI using ‘real-time’ queries and putting analytic results in the hands of their business and analytic teams to bring more insights and analytic value to customers data, and drive outcomes. |
| OpenAI and Azure OpenAI | Teradata API integration with OpenAI and Azure OpenAI provides customers with the necessary tools to operationalize analytic workflows on their Vantage platform. This integration allows for the connection of their integrated enterprise data with OpenAI and Azure OpenAI using 'real-time' queries. The analytic results are then made accessible to their business and analytic teams, enabling them to derive more insights and analytic value from their data, and consequently drive better outcomes. |
How Do I Use this Content?
Use this guide as a reference to find detailed descriptions, usage notes, and use cases of functions available in the API Integration packages.
How Do I Get Started?
| Platform | Description |
|---|---|
| Azure Machine Learning | Prerequisites Teradata' integration with Azure Machine Learning services requires setting up Azure Machine Learning Endpoints. |
| Installation Contact Teradata to install the API_Request in-database function. Install the teradataml extension library through PIP from https://pypi.org/project/tdapiclient/ in your Python environment. |
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| Google Vertex AI | Prerequisites Teradata’s integration with Google Vertex AI services requires setting up Google Service Account with access to Vertex AI and generating an access-token to be used as AUTHORIZATION in Teradata. |
| Installation Contact Teradata to install the API_Request in-database function. Install the teradataml extension library through PIP from https://pypi.org/project/tdapiclient/ in your Python environment. |
|
| OpenAI | Prerequisites Teradata’s integration with OpenAI services requires setting up OpenAI service account with access to OpenAI and necessary key to be used as AUTHORIZATION in Teradata to call the OpenAI API. |
| Installation Contact Teradata to install the API_Request in-database function. Install the teradataml extension library through PIP from https://pypi.org/project/tdapiclient/ in your Python environment. |
|
| Azure OpenAI | Prerequisites Teradata’s integration with Azure OpenAI services requires setting up Azure OpenAI service account. You should have the correct deployment in Azure OpenAI with necessary keys and endpoint information to call the Azure OpenAI API. |
| Installation Contact Teradata to install the API_Request in-database function. Install the teradataml extension library through PIP from https://pypi.org/project/tdapiclient/ in your Python environment. |
References to Other Relevant Content
| Platform | Description |
|---|---|
| Azure Machine Learning | Teradata
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| Azure | |
| Google Vertex AI | Teradata
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| Google Vertex AI | |
| OpenAI | Teradata
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| OpenAI | |
| Azure OpenAI | Teradata
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| Azure OpenAI |