Agent Platform Vizier

Related topics

• Agent Platform Vizier works with BigQuery ML From the official documentation: “Hyperparameter tune a BigQuery ML model with Agent Platform Vizier”
• Agent Platform Vizier works with Gemini Enterprise Agent Platform From the official documentation: “Learn how to use Agent Platform Vizier when training with Gemini Enterprise Agent Platform.”
• Hyperparameter tuning for custom training depends on Agent Platform Vizier From the official documentation: “[Hyperparameter tuning for custom training](/gemini-enterprise-agent-platform/machine-learning/training/hyperparameter-tuning-overview) is a built-in feature that uses Agent Platform Vizier for training jobs.”
• BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
• BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
• BigQuery works with BigQuery DataFrames From the official documentation: “Uploads a pandas DataFrame to BigQuery using BigQuery DataFrames.”
• BigQuery ML works with Cloud Storage From the official documentation: “You can export BigQuery ML models to Cloud Storage, and use them for online prediction, or edit them in Python.”
• 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.”
• BigQuery ML works with Model Registry From the official documentation: “Automatically register a BigQuery ML model to Model Registry”
• AutoML is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform offers two model training methods, AutoML and custom training.”
• BigQuery ML works with Gemini Enterprise Agent Platform From the official documentation: “You can also access Agent Platform models by using BigQuery ML.”
• Gemini Enterprise Agent Platform works with Vertex Explainable AI From the official documentation: “Feature attributions are included in Agent Platform inferences through [Vertex Explainable AI](/gemini-enterprise-agent-platform/machine-learning/tabular-data/classification-explanations).”
• Model Registry is part of Gemini Enterprise Agent Platform From the official documentation: “Import a pretrained TensorFlow model into the Gemini Enterprise Agent Platform Model Registry.”
• BigQuery ML is part of BigQuery From the official documentation: “BigQuery ML lets you use SQL in BigQuery to perform machine learning (ML) and predictive analytics.”
• BigQuery DataFrames depends on BigQuery From the official documentation: “BigQuery DataFrames provides a Python DataFrame and machine learning (ML) API powered by the BigQuery engine.”
• Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your image AutoML Edge models to Cloud Storage.”
• AutoML works with BigQuery ML From the official documentation: “If your tabular data is stored in BigQuery ML, you can train an AutoML tabular model directly in BigQuery ML.”
• AutoML works with Gemini Enterprise Agent Platform From the official documentation: “Learn how to use `AutoML` for training with `Gemini Enterprise Agent Platform`.”
• AutoML is an alternative to Gemini Enterprise Agent Platform From the official documentation: “Perform serverless training on Gemini Enterprise Agent Platform to run your own machine learning (ML) training code in the cloud, instead of using AutoML.”
• BigQuery works with BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
• BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
• BigQuery DataFrames is an alternative to BigQuery ML From the official documentation: “Use BigQuery ML, BigQuery DataFrames, or Managed Service for Apache Spark to perform distributed data processing on large datasets.”
• BigQuery DataFrames works with Cloud Storage From the official documentation: “To save your BigQuery DataFrames to local files or Cloud Storage files using the [`to_csv`](https://dataframes.bigquery.dev/reference/api/bigframes.pandas.DataFrame.to_csv.html) method, use the following code sample:”
• BigQuery DataFrames depends on Gemini Enterprise Agent Platform From the official documentation: “BigQuery DataFrames uses BigQuery, Cloud Run functions, Agent Platform, and other Google Cloud services, which incur their own costs.”
• BigQuery ML works with Vertex Explainable AI From the official documentation: “This explainability feature is integrated with [BigQuery ML](/bigquery/docs/xai-overview) and [Vertex Explainable AI](/vertex-ai/docs/explainable-ai/overview), which provide feature-based explanations.”
• Gemini Enterprise Agent Platform depends on Cloud Storage From the official documentation: “Certain tasks in Agent Platform require that you use additional Google Cloud products besides Agent Platform. For example, in most cases, you must use Cloud Storage and Artifact Registry when you create a custom training pipeline.”
• Vertex Explainable AI depends on Gemini Enterprise Agent Platform From the official documentation: “Vertex Explainable AI isn't available using exported tabular models. If you need to use Vertex Explainable AI, you must serve predictions from a model hosted by Agent Platform.”