Vertex AI Workbench
The single development environment for the entire data science workflow.
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Natively analyze your data with a reduction in context switching between services
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Data to training at scale. Build and train models 5X faster, compared to traditional notebooks
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Scale up model development with simple connectivity to Vertex AI services
Benefits
Easy exploration and analysis
Simplified access to data and in-notebook access to machine learning with BigQuery, Dataproc, Spark, and Vertex AI integration.
Rapid prototyping and model development
Take advantage of the power of infinite compute with Vertex AI training for experimentation and prototyping, to go from data to training at scale.
End-to-end notebook workflows
Using Vertex AI Workbench you can implement your training, and deployment workflows on Vertex AI from one place.
Key features
Key features
Fully managed compute
A Jupyter-based fully managed, scalable, enterprise-ready compute infrastructure with security controls and user management capabilities.
Interactive data and ML experience
Explore data and train ML models with easy connections to Google Cloud's big data solutions.
Portal to complete end-to-end ML training
Develop and deploy AI solutions on Vertex AI with minimal transition.
Documentation
Technical resources
Vertex AI Workbench documentation
Learn more about Vertex AI Workbench.
Vertex AI documentation
Explore Vertex AI product documentation, from introductory to advanced.
Explore end-to-end ML on Vertex AI Workbench in this Codelab
In this lab, you'll learn how to use Vertex AI Workbench for data exploration and ML model training.
Build an image classification model in this Codelab
In this lab, you'll learn how to configure and launch notebook executions with Vertex AI Workbench.
Create a managed notebooks instance
This in-console tutorial takes you through a step-by-step guide about how to create a managed notebooks instance.
Create a user-managed notebooks instance
This in-console tutorial takes you through a step-by-step guide about how to create a user-managed notebooks instance.
All features
All features
| Simplified data access | Extensions will seamlessly connect to the entire data estate including BigQuery, Data Lake, Dataproc, and Spark. Seamlessly scale up or scale out depending on your analytic and AI needs. |
| Explore data sources using a catalog | Write SQL, Spark queries from a syntax-aware, auto-complete enabled notebook cell. |
| Data visualization | Integrated, intelligent visualization tools will provide easy insights into data. |
| Hands-off, cost-effective infrastructure | All aspects of the compute are managed. Idle timeout and auto shutdown will optimize total cost of ownership. |
| Enterprise security, simplified | Out-of-the-box Google Cloud security controls. Single sign-on and simple authentication to other Google Cloud services. |
| Data Lake and Spark in one place | Whether you use TensorFlow, PyTorch, or Spark, you can run any engine from Vertex AI Workbench. |
| Deep Git, training, and MLOps integration | With few clicks, plug notebooks into established Ops workflows. Use notebooks for distributed training, hyper-parameter optimization, or scheduled or triggered continuous training. Deep integration with Vertex AI services brings MLOps into the notebook without the need to rewrite code or new workflows. |
| Seamless CI/CD | Kubeflow Pipelines integration to use Notebooks as an ideal, tested, and verified deployment target. |
| Notebook viewer | Share output of periodically updated notebook cells for reporting and bookkeeping purposes. |
Pricing
Pricing
Vertex AI Workbench's pricing details can be found
here.
The pricing model is based upon compute and services based
on the infrastructure you use, as well as other services
consumed from within Vertex AI Workbench.