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https://dev.mysql.com/doc/relnotes/connector-j/en/news-9-5-0.html
Note These release notes were created with the assistance of MySQL HeatWave GenAI. MySQL Connector/J 9.5.0 supersedes 9.4 and is recommended for use on production systems. This release can be used against MySQL Server version 8.0 and later. It ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-data-drift-detection.html
MySQL AI includes data drift detection for classification and regression models. Before You Begin Review how to Create a Machine Learning Model. Data Drift Detection Overview Machine learning typically makes an assumption that the training data and ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-ml-model-metadata.html
The model_metadata column in the model catalog allows you to view detailed information on trained models. For example, you can view the algorithm used to train the model, the columns in the training table, and values for the model explanation. When ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-scoring-a-recommendation-model.html
After generating predicted ratings/rankings and recommendations, you can score the model to assess its reliability. For a list of scoring metrics you can use with recommendation models, see Recommendation Model Metrics. For this use case, you use ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-using-a-recommendation-model-items-to-users.html
This topic describes how to generate recommended users for items. For known users and known items, the output includes a list of users that will most likely give a high rating to an item and will also predict the ratings or rankings. For a new ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-using-a-recommendation-model-users-to-items.html
This topic describes how to generate recommended items for users. For known users and known items, the output includes a list of items that the user will most likely give a high rating and the predicted rating or ranking. For a new user, and an ...
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/mysql-ai/9.5/en/mys-mysqlai-ml-embed-table.html
The ML_EMBED_TABLE routine runs multiple embedding generations in a batch, in parallel. This topic contains the following sections: ML_EMBED_TABLE Syntax Syntax Examples See Also To learn about the privileges you need to run this routine, see ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-generate-table.html
The ML_GENERATE_TABLE routine runs multiple text generation or summarization queries in a batch, in parallel. The output generated for every input query is the same as the output generated by the ML_GENERATE routine. This topic contains the ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-mysqlai-ml-generate.html
The ML_GENERATE routine uses the specified large language model (LLM) to generate text-based content as a response for the given natural-language query. It can include the following parameters: task: specifies the task expected from the LLM.