Vertex ML Metadata

Related topics

• Agent Platform Pipelines works with Vertex ML Metadata From the official documentation: “In figure 5, Vertex ML Metadata serves as the ML metadata store for Agent Platform Pipelines.”
• Gemini Enterprise Agent Platform Pipelines works with Vertex ML Metadata From the official documentation: “Agent Platform Pipelines uses Vertex ML Metadata to store the metadata created by your pipeline runs.”
• Experiments on Gemini Enterprise Agent Platform works with Vertex ML Metadata From the official documentation: “Use Experiments on Agent Platform to integrate with Vertex ML Metadata and to log and build linkage across parameters, metrics, and dataset and model artifacts.”
• Knowledge Catalog is an alternative to Vertex ML Metadata From the official documentation: “To view and analyze the pipeline artifact lineage, you can use either Vertex ML Metadata or Knowledge Catalog.”
• Vertex AI Experiments depends on Vertex ML Metadata From the official documentation: “Vertex AI Experiments requires and depends on [Vertex ML Metadata](/gemini-enterprise-agent-platform/machine-learning/ml-metadata/introduction).”
• Vertex ML Metadata is part of TensorFlow Extended From the official documentation: “MLMD is an integral part of TensorFlow Extended (TFX), but is designed so that it can be used independently.”
• 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 Knowledge Catalog From the official documentation: “Some Knowledge Catalog functionalities trigger job execution using BigQuery.”
• 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.”
• Agent Platform Pipelines works with TensorFlow Extended From the official documentation: “You can batch run ML pipelines defined using the Kubeflow Pipelines or the TensorFlow Extended (TFX) framework.”
• Vertex AI Experiments works with Vertex AI TensorBoard From the official documentation: “Vertex AI Experiments stores a reference to the Vertex TensorBoard resource.”
• Vertex AI TensorBoard depends on Cloud Storage From the official documentation: “You must have a Cloud Storage bucket to store Vertex AI TensorBoard logs.”
• Agent Platform Pipelines works with Kubeflow Pipelines From the official documentation: “You can batch run ML pipelines defined using the Kubeflow Pipelines or the TensorFlow Extended (TFX) framework.”
• Agent Platform Pipelines works with Vertex AI TensorBoard From the official documentation: “To see an example of TensorBoard integration with pipelines, run the " TensorBoard integration with Pipelines" notebook in one of the following environments:”
• 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.”
• Agent Platform ML Metadata works with Agent Platform Pipelines From the official documentation: “When you run a pipeline using Agent Platform Pipelines, all parameters and artifact metadata consumed and generated by the pipeline are stored in Agent Platform ML Metadata.”
• Agent Platform ML Metadata is an alternative to Knowledge Catalog From the official documentation: “To view and analyze the lineage of pipeline artifacts for workloads in Agent Platform, you can use [Agent Platform ML Metadata](/gemini-enterprise-agent-platform/machine-learning/ml-metadata/introduction) or [Knowledge Catalog](/dataplex/docs/introduction).”
• Agent Platform ML Metadata works with Vertex AI Experiments From the official documentation: “Agent Platform Experiments, along with [Gemini Enterprise Agent Platform ML Metadata](/gemini-enterprise-agent-platform/machine-learning/ml-metadata/introduction), provides a way to find the artifacts tracked in an experiment.”
• Agent Platform Pipelines depends on Cloud Storage From the official documentation: “Agent Platform Pipelines stores the artifacts of your pipeline runs using Cloud Storage.”
• Agent Platform Pipelines is part of Gemini Enterprise Agent Platform From the official documentation: “With Agent Platform Pipelines, you can use BigQuery operators to plug any BigQuery jobs (including BigQuery ML) into an ML pipeline.”
• 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 works with Gemini Enterprise Agent Platform Pipelines From the official documentation: “To learn more about using Vertex AI Pipelines with BigQuery, see [Export and deploy a BigQuery machine learning model for prediction](https://codelabs.developers.google.com/codelabs/bqml-vertex-prediction#0).”
• Cloud Storage works with Gemini Enterprise Agent Platform Pipelines From the official documentation: “Agent Platform Pipelines uses Cloud Storage to store the artifacts of your pipeline runs.”
• Cloud Storage works with Knowledge Catalog From the official documentation: “To automatically discover and build inventories of the data assets, integrate Knowledge Catalog with your storage systems, such as [BigQuery](/bigquery/docs/introduction), [Cloud Storage](/storage/docs/introduction), and [Pub/Sub](/pubsub/docs/overview).”
• 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.”
• Gemini Enterprise Agent Platform works with Knowledge Catalog From the official documentation: “Knowledge Catalog provides a way to search for your Agent Platform models across projects and regions.”
• Gemini Enterprise Agent Platform Pipelines is part of Gemini Enterprise Agent Platform From the official documentation: “You can configure Private Service Connect interface connections for certain resources in Gemini Enterprise Agent Platform, including: - [Ray on Vertex AI](/gemini-enterprise-agent-platform/machine-learning/open-source/ray/create-cluster#enable_interface) - [Custom…”
• Gemini Enterprise Agent Platform Pipelines works with Kubeflow Pipelines From the official documentation: “[Gemini Enterprise Agent Platform Pipelines](/gemini-enterprise-agent-platform/machine-learning/pipelines/introduction) is a serverless service that runs Kubeflow pipelines.”
• Gemini Enterprise Agent Platform Pipelines works with Vertex AI TensorBoard From the official documentation: “Compile and execute the KFP pipeline in Gemini Enterprise Agent Platform Pipelines with Vertex AI TensorBoard enabled for near real time monitoring.”
• Knowledge Catalog depends on Cloud Storage From the official documentation: “Looker (Google Cloud core) publishes LookML metadata to regional Cloud Storage repositories for Knowledge Catalog to consume through a batch sync process.”
• Kubeflow Pipelines is an alternative to TensorFlow Extended From the official documentation: “You can define the pipeline as a DAG using either the Kubeflow Pipelines SDK or the TFX SDK, compile it to its YAML for intermediate representation, and then run the pipeline.”
• Vertex AI Experiments is part of Agent Platform ML Metadata From the official documentation: “Gemini Enterprise Agent Platform Experiments is a context in [Agent Platform ML Metadata](/gemini-enterprise-agent-platform/machine-learning/ml-metadata/introduction) where an experiment can contain *n* experiment runs in addition to *n* pipeline runs.”
• Vertex AI Experiments depends on Vertex AI TensorBoard From the official documentation: “To log time series metrics, Vertex AI Experiments requires a backing Vertex AI TensorBoard instance.”
• Vertex AI TensorBoard is part of Gemini Enterprise Agent Platform From the official documentation: “Require customer-managed encryption keys (CMEK) for TensorBoard on Gemini Enterprise Agent Platform to gain more control over the encryption of experiment data and model visualizations.”