TensorFlow Extended

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.”
• Dataflow works with TensorFlow Extended From the official documentation: “TensorFlow Extended (TFX) lets you deploy complete ML pipelines by using an orchestration framework that has a built-in integration with Apache Beam and the Dataflow runner.”
• 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 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.”
• Cloud Storage works with Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
• 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.”
• Dataflow depends on Cloud Storage From the official documentation: “Begin by creating a Cloud Storage bucket using the Google Cloud console or Google Cloud CLI. The Dataflow pipeline uses this bucket as a temporary storage location.”
• 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.”
• Agent Platform Pipelines depends on Cloud Storage From the official documentation: “Agent Platform Pipelines stores the artifacts of your pipeline runs using Cloud Storage.”
• 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 Dataflow From the official documentation: “To use Artifact Registry with Dataflow, you must grant at least [Artifact Registry Writer access](/artifact-registry/docs/access-control#permissions) (`role/artifactregistry.writer`) to the [worker service account](#worker-service-account) that runs the Dataflow job.”
• 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.”
• 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.”
• Dataflow works with Vertex AI From the official documentation: “Vertex AI models are subject to [Rate Limits and Quotas](https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#view-the-quotas-by-region-and-by-model) and Dataflow automatically retries throttled requests with exponential backoff.”
• 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.”
• 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.”