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https://dev.mysql.com/doc/mysql-router/9.4/en/router-read-replicas.html
MySQL Router reads the values defined in the metadata field, v2_router_options.router_options.read_only_targets, to retrieve routing information for read-only traffic. v2_router_options.router_options.read_only_targets is populated by the AdminAPI ...
https://dev.mysql.com/doc/mysql-router/9.4/en/router-read-write-splitting-configuration.html
To enable read-write splitting, the following router options must be enabled: access_mode: must be set to auto. Note It is possible to define read_write and read_only access_mode values per session, only. destinations: must be set to a ...
https://dev.mysql.com/doc/mysql-router/9.4/en/router-read-write-splitting-statements.html
The following describes read-only statements: Statements are read-only if they start with: SELECT DO VALUES TABLE WITH that is not followed by UPDATE or DELETE. EXPLAIN, DESCRIBE, or DESC which are not followed by UPDATE or DELETE. If they start ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/index.html
It covers how to load data, run queries, optimize analytics workloads, and use machine learning and generative AI capabilities. For help with using MySQL, please visit the MySQL Forums, where you can discuss your issues with other MySQL users.
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-anomaly-detection.html
Anomaly detection, which is also known as outlier detection, is the machine learning task that finds unusual patterns in data. The following tasks use datasets generated by OCI GenAI using Meta Llama Models. The anomaly detection use-cases are to ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-classification-score.html
After generating predictions and explanations, you can score the model to assess its reliability. For a list of scoring metrics you can use with classification models, see Classification Metrics. For this use case, you use the test dataset for ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-ease-of-use.html
The AutoML feature of MySQL AI is purpose-built for ease of use. With AutoML and a set of training data, you can train a predictive machine learning model with a single call to the ML_TRAIN SQL routine. For example: CALL ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-explanations.html
Prediction explanations are generated by running ML_EXPLAIN_ROW or ML_EXPLAIN_TABLE on unlabeled data. The data must have the same feature columns as the data used to train the model. Prediction explanations are similar to model explanations, but ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-get-started.html
The topics in this section go through the process of training and using a machine learning model. Before going through these tasks, make sure to Review Additional AutoML Requirements. To start using AutoML with sample datasets, see Machine Learning ...
https://dev.mysql.com/doc/mysql-ai/9.5/en/mys-ai-aml-limitations.html
Text Handling Limitations AutoML only supports datasets in the English language. MySQL HeatWave AutoML does not support TEXT columns with NULL values. MySQL HeatWave AutoML does not support target columns (a column with ground truth values) with a ...