Streamline ML Operations with Google Cloud and ZenML

Integrate the power of Google Cloud's scalable infrastructure and managed services with ZenML's MLOps capabilities. This integration enables seamless orchestration of ML workflows on Google Cloud, leveraging Vertex AI Pipelines for serverless, production-ready pipeline execution.

Features with ZenML

Effortless orchestration of ZenML pipelines on Google Cloud's Vertex AI Pipelines
Serverless, scalable execution of ML workflows without provisioning infrastructure
Seamless integration with Google Cloud's managed services for data storage, processing, and model serving
Enhanced visibility and monitoring of pipeline runs through Vertex AI's intuitive UI
Simplified deployment and reproducibility of ML workflows across Google Cloud environments

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Main Features

Scalable, serverless infrastructure for ML workloads
Managed services for data storage, processing, and model serving
Vertex AI Pipelines for orchestrating end-to-end ML workflows
Integrated monitoring, logging, and visualization of ML pipeline runs
Seamless integration with Google Cloud's AI Platform for model training and deployment

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How to use ZenML with

Google Cloud


# Once registered in the frontend, this is all you need to do
# for all your pipelines to be run on gcp
zenml integration install gcp
zenml stack set ...

This code snippet demonstrates how to run your ML workloads in GCP with ZenML. You simply register the stack within the dashboard. Set this created stack as your active stack in the CLI. All your ZenML pipelines will now magically run on GCP.

Additional Resources

Getting started with ZenML and GCP