hyperparameter tuning job

Related topics

• hyperparameter tuning job is part of Gemini Enterprise Agent Platform From the official documentation: “There are three types of Agent Platform resources you can create to train custom models on Agent Platform: - [Custom jobs](/gemini-enterprise-agent-platform/machine-learning/training/create-custom-job) - [Hyperparameter tuning…”
• hyperparameter tuning job is part of training pipeline From the official documentation: “This training pipeline encapsulates a hyperparameter tuning job with an added convenience step that makes it easier to deploy your model to Agent Platform after training.”
• StudySpec is part of hyperparameter tuning job From the official documentation: “"study_spec": { "metrics": [metric], "parameters": [parameter], "algorithm": aiplatform.gapic.StudySpec.Algorithm.RANDOM_SEARCH, }, "trial_job_spec": {"worker_pool_specs": [worker_pool_spec]},”
• BigQuery works with Gemini Enterprise Agent Platform From the official documentation: “The BigQuery dataset stores all versions for the associated Gemini Enterprise Agent Platform dataset.”
• training pipeline depends on dataset From the official documentation: “When you have a dataset with a representative set of training items, you are ready to create an AutoML Edge training pipeline.”
• BigQuery works with training pipeline From the official documentation: “If you use a managed dataset in an Agent Platform training pipeline that utilizes Ray for distributed training, the data from the managed dataset is made available to the training containers, which your Ray application can then access (via mounted Cloud Storage or BigQuery if…”
• dataset is part of BigQuery From the official documentation: “Create a BigQuery dataset to store your models and sample data:”
• dataset is part of Gemini Enterprise Agent Platform From the official documentation: “This resource always executes the training task, and optionally may also export data from a Gemini Enterprise Agent Platform `Dataset` which becomes the training input, upload the Model to Gemini Enterprise Agent Platform, and evaluate the Model.”
• Model is part of Gemini Enterprise Agent Platform From the official documentation: “`Model`, this resource is available in Gemini Enterprise Agent Platform.”
• Model is part of training pipeline From the official documentation: “TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder() .setDisplayName(displayName) .setInputDataConfig(inputDataConfig) .setTrainingTaskDefinition(customTaskDefinition) .setTrainingTaskInputs(trainingTaskInputs) .setModelToUpload(model) .build();”
• training pipeline is part of Gemini Enterprise Agent Platform From the official documentation: “There are three types of Agent Platform resources you can create to train custom models on Agent Platform: - [Custom jobs](/gemini-enterprise-agent-platform/machine-learning/training/create-custom-job) - [Hyperparameter tuning…”