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https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-forecasting-model-types.html
This topic describes the types of forecasting models supported by AutoML. Univariate Models In a univariate model, you define one numeric column as an endogenous variable, specified as a JSON_ARRAY. For example, you forecast the rainfall for the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-handles.html
When ML_TRAIN trains a model, you have the option to specify a name for the model, which is the model handle. If you do not specify a model handle name, a model handle is automatically generated that is based on the database name, input table name, ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-model-viewing.html
To view the details for the models in your model catalog, query the MODEL_CATALOG table. Before You Begin Review the following: Create a Machine Learning Model The Model Catalog View Details for Your Models The following example queries model_id, ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-supported-data-types.html
AutoML has the following limitations for text usage: The ML_PREDICT_TABLE ml_results column contains the prediction results and the data. What's Next Learn more about the following: Additional AutoML Requirements AutoML Privileges Learn how to ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-training-a-recommendation-model.html
After preparing the data for a recommendation model, you can train the model. Requirements for Recommendation Training Define the following as required to train a recommendation model. Set the task parameter to recommendation to train a ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-generate-content.html
The following sections in this topic describe how to generate new text-based content using the GenAI feature of MySQL AI: Before You Begin Generating Content Running Batch Queries What's Next Before You Begin Review the GenAI requirements and ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-genai-generate-embeddings.html
This section describes how to generate vector embeddings using the ML_EMBED_ROW routine. Vector embeddings are a numerical representation of the text that capture the semantics of the data and relationships to other data. You can pass the text ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-embed-row.html
The ML_EMBED_ROW routine uses the specified embedding model to encode the specified text or query into a vector embedding. The routine returns a VECTOR that contains a numerical representation of the specified text. This topic contains the ...
https://dev.mysql.com/doc/internals/en/generating-browsable-binary-log-information.html
Source files in the sql directory of a MySQL source tree or source distribution contain comments that can be processed with doxygen to generate HTML files that describe classes, files, and so forth. To generate the HTML files and view information ...
https://dev.mysql.com/doc/connectors/en/connector-python-asyncio.html
Installing Connector/Python also installs the mysql.connector.aio package that integrates asyncio with the connector to allow integrating asynchronous MySQL interactions with an application. Functions included in the asyncio API must be used to ...