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https://dev.mysql.com/doc/mysql-monitor/8.0/en/mem-advisors-variables.html
When MySQL Enterprise Monitor evaluates an expression, it replaces variables with values. Variables can be used in the Description, Advice, Action, and Links attributes of a advisor, as well as in expressions. For instance, you can add the message, ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-async-exec-prepared-stmt.html
The execute_prepared_stmt_async routine creates a task that executes SQL statements asynchronously within an event stored in the schema_name schema. schema_name (VARCHAR(255)): specifies the schema to run the given SQL statements. If the schema is ...
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai-authenticate-service.html
Note If the dynamic group belongs to the default identity domain, you can omit specifying the identity domain name. GroupName: the dynamic group name CompartmentID: the compartment ID of the DB system For more information, see Resource Principals.
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-prepare-an-anomaly-detection-model.html
This topic describes how to prepare the data to use for two anomaly detection machine learning models: a semi-supervised anomaly detection model, and an unsupervised anomaly detection model for logs. To prepare the data for this use case, you set ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-topic-modeling-train.html
After preparing the data for topic modeling, you can train the model. Before You Begin Review and complete all the tasks to Prepare Data for Topic Modeling. Requirements for Topic Modeling Training Define the following required parameters for topic ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-training-a-recommendation-model.html
After preparing the data for a recommendation model, you can train the model. Before You Begin Requirements for Recommendation Training Options for Recommendation Models with Explicit Feedback Options for Recommendation Models with Implicit ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-using-an-anomaly-detection-model.html
To generate predictions, use the sample data from the two anomaly detection datasets: credit_card_train and training_data. Both datasets have labeled and unlabeled rows, but only the dataset for semi-supervised learning uses this for training. The ...
https://dev.mysql.com/doc/heatwave-aws/en/heatwave-aws-genai-external-llms.html
MySQL HeatWave GenAI External LLMs 14.2.3 MySQL HeatWave GenAI External LLMs MySQL HeatWave GenAI supports external LLMs provided by Amazon Bedrock. When external LLMs are used, data leaves your MySQL HeatWave Cluster to be processed by Amazon ...
https://dev.mysql.com/doc/heatwave-aws/en/heatwave-aws-genai-view-models.html
View Available Models 14.2.1 View Available Models As of MySQL 9.3.2, once you are connected to a DB System, you can view the list of available models and information on them with the ML_SUPPORTED_LLMS view inside the sys schema of the DB System: ...
https://dev.mysql.com/doc/heatwave-aws/en/heatwave-aws-hw-workload-perf-data.html
MySQL HeatWave Workload Performance Data 17.2.2.1 MySQL HeatWave Workload Performance Data The HeatWave Workload tab provides the following performance data for your MySQL HeatWave Clusters: Information is shown for the last 1000 query executions.