Agent Platform Pipelines

Related topics

• 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.”
• 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.”
• 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 Kubeflow Pipelines SDK From the official documentation: “Agent Platform Pipelines lets you run machine learning (ML) pipelines that were built using the Kubeflow Pipelines SDK or TensorFlow Extended in a serverless manner.”
• 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:”
• 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 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.”
• Agent Platform Pipelines works with Metrics Explorer From the official documentation: “You can view the following metrics related to Agent Platform Pipelines in the Metrics Explorer:”
• Agent Platform Pipelines works with vector search From the official documentation: “Pipelines allows for building automated pipelines to generate embeddings, create and update Vector Search indexes, and form an MLOps setup for production search and recommendation systems.”
• 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.”
• Vertex AI is part of Gemini Enterprise Agent Platform From the official documentation: “Vertex AI's services are now part of Gemini Enterprise Agent Platform.”
• 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.”
• BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
• 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.”
• Gemini Enterprise Agent Platform Pipelines works with Kubeflow Pipelines SDK From the official documentation: “KFP is the most commonly used framework for authoring and orchestrating pipelines in 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.”
• vector search works with Vertex AI From the official documentation: “Reference architecture for a RAG-capable generative AI application using Vertex AI and Vector Search.”
• 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.”
• Artifact Registry depends on Cloud Storage From the official documentation: “The registry service hosts your custom images in Cloud Storage under your Google Cloud project.”
• Artifact Registry works with Cloud Storage From the official documentation: “| Artifact Registry | Stored in Cloud Storage, for each data chunk | |”
• Artifact Registry is an alternative to Cloud Storage From the official documentation: “Re-upload the template or definition to Artifact Registry or Cloud Storage.”
• Artifact Registry works with Gemini Enterprise Agent Platform From the official documentation: “Agent Platform supports Gemini Enterprise Agent Platform serverless training with container images on Artifact Registry or Docker Hub.”
• Artifact Registry works with Gemini Enterprise Agent Platform Pipelines From the official documentation: “After you've uploaded your pipeline template to your repository in Artifact Registry, it is ready to be used in Agent Platform Pipelines.”
• Artifact Registry works with Kubeflow Pipelines From the official documentation: “In this section, you configure a Kubeflow Pipelines SDK registry client and upload your compiled pipeline template to Artifact Registry from your JupyterLab notebook.”
• Artifact Registry works with Metrics Explorer From the official documentation: “Metrics Explorer lets you chart Artifact Registry request-count metrics so that you can monitor API traffic across your repositories.”
• 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).”
• Cloud Storage works with vector search From the official documentation: “Cloud Storage: You can use the embeddings from a Cloud Storage bucket for building vector indexes and performing Vector Search.”
• Gemini Enterprise Agent Platform depends on Artifact Registry 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 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.”
• 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.”
• 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.”
• Kubeflow Pipelines is part of Vertex AI From the official documentation: “Integrate serverless batch pipelines and catalog operations with orchestration workflows using Managed Service for Apache Airflow (formerly Cloud Composer) and Kubeflow Pipelines in Vertex AI.”
• vector search is part of BigQuery From the official documentation: “This document provides an overview of embeddings and vector search in BigQuery.”
• vector search is part of Vertex AI From the official documentation: “Vertex AI features for AutoML Forecasting, AutoML Tabular, Batch Prediction, Online Prediction, Pipelines, Training, Vector Search, and Vizier are available in [10 additional Google Cloud regions](/vertex-ai/docs/general/locations#feature-availability).”
• 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 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 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.”
• 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.”