Build, deploy, and monitor production ML and generative AI solutions on Gemini Enterprise Agent Platform, and track your readiness for the Professional Machine Learning Engineer exam.
Points to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
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.”
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.”
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.”
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.”
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.”
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.”
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.”
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 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.”
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.”