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AI Platform Notebooks

An enterprise notebook service to launch projects in minutes

AI Platform Notebooks is a managed service whose integrated JupyterLab environment makes it easy to create instances that come pre-installed with the latest data science and ML frameworks and integrate with BigQuery, Cloud Dataproc, and Cloud Dataflow for easy development and deployment.

Preconfigured virtual machines for deep learning applications

Deep Learning VM Image makes it easy and fast to provision a VM quickly and effortlessly, with everything you need to get your deep learning project started on Google Cloud. You can launch Compute Engine instances pre-installed with popular ML frameworks like TensorFlow, PyTorch, or scikit-learn, and add Cloud TPU and GPU support with a single click.

Preconfigured and optimized containers for deep learning environments

Build your deep learning project quickly with a portable and consistent environment for developing, testing, and deploying your AI applications on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises.

Data preparation for machine learning model training

Use the AI Platform Data Labeling Service to request having human labelers label a collection of data that you plan to use to train a custom machine learning model. You can submit the representative samples to human labelers who annotate them with the "right answers" and return the dataset in a format suitable for training a machine learning model.

Distributed training with automatic hyper parameter tuning

Use AI Platform to run your TensorFlow, scikit-learn, and XGBoost training applications in the cloud. You can also use custom containers to run training jobs with other machine learning frameworks.

Model hosting service with serverless scaling

Host your trained machine learning models in the cloud and use AI Platform Prediction to infer target values for new data.

Model optimization using ground truth labels

Sample the prediction from trained machine learning models that you have deployed to AI Platform and provide ground truth labels for your prediction input using the continuous evaluation capability. The Data Labeling Service compares your models' predictions with the ground truth labels to provide continual feedback on your model performance.

Model evaluation and understanding using a code-free visual interface

Investigate model performances for a range of features in your dataset, optimization strategies, and even manipulations to individual datapoint values using the What-If Tool integrated with AI Platform.

Hardware designed for performance

Cloud TPUs are a family of hardware accelerators that Google designed and optimized specifically to speed up and scale up machine learning workloads for training and inference programmed with TensorFlow. Cloud TPUs are designed to deliver the best performance per dollar for targeted TensorFlow workloads and to enable ML engineers and researchers to iterate more quickly.

The machine learning toolkit for Kubernetes

Kubeflow makes deployments of machine learning workflows on Kubernetes simple, portable, and scalable by providing a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.

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AI and machine learning products - Cloud AI | Google Cloud