Notebooks

An enterprise notebook service to get your projects up and running in minutes.

Description of what the video is about.

Managed JupyterLab notebook instances

Notebooks is a managed service that offers an integrated and secure JupyterLab environment for data scientists and machine learning developers to experiment, develop, and deploy models into production. Users can create instances running JupyterLab that come pre-installed with the latest data science and machine learning frameworks in a single click. 

What's new

Deploy with one click

You can deploy new JupyterLab instances with one click and start analyzing your data immediately. Each instance comes pre-configured with optimized versions of the most popular data science and machine learning libraries including TensorFlow, Keras, PyTorch, fast.ai, RAPIDS, NumPy, scikit-learn, pandas, and Matplotlib.

Scale on demand

You can start small and scale up by adding CPUs, RAM, and GPUs. When your data gets too big for one machine, seamlessly switch to distributed services like BigQuery, Dataproc, Dataflow, and Vertex Training and Prediction. You pay for the instances only while they are running.

Seamless experience

You’ll go from data to a deployed machine learning model without leaving Notebooks. Pull data from BigQuery, use Cloud Dataproc to transform it, and leverage Vertex AI services or Kubeflow for distributed training and online prediction.

Features

Managed JupyterLab experience

Notebooks is built on the industry standard JupyterLab. So you can use it with the RPython and R data science community and customize your environment by installing JupyterLab plugins. 

Secure development

Notebooks supports popular enterprise security architectures through VPC-SC, shared VPC, and private IP controls. You can also encrypt your data on disk with CMEK.

Controlled user access

You can choose between two predefined user access modes: restrict Notebooks to a single-user or use a service account. You can also customize access based on your enterprise security architecture based on Cloud Identity and Access Management.

Advanced networking

You can select any virtual private cloud for their Notebook instances, provided that they have access either through Google Private Access or the internet to Cloud Storage. You can also turn off public IP address and access your instance via proxy.

Support for data science frameworks

We provide a pre-configured environment that supports the most popular data science libraries, including R, pandas, NumPy, SciPy, scikit-learn, and Matplotlib, and ML frameworks like TensorFlow, Keras, fast.ai, RAPIDS, XGBoost, and PyTorch.

Optimized for machine learning

Notebooks' optimized versions of TensorFlow and PyTorch enable you to get the most out of Google Cloud hardware and seamlessly add and remove GPUs from your instance.

Git support

It’s easy to pull and push notebooks from your Git repository, making it also easy to share your notebooks with colleagues.

Bring your own container

You can run a Notebook instance on a container of your choice. This provides you the flexibility to install specific libraries mandated by your organization or preconfigure the environment running JupyterLab to your preference.

Explainable AI support

Notebooks come pre-installed with Google Cloud's Explainable AI, which allows you to generate feature attributions on-the-fly for rapid model prototyping and debugging.

End-to-end machine learning life cycle

At top reads Vertex AI. With arrows moving from left to right, there are 4 pipeline columns. 1 Prepare, lists Data labeling, BigQuery datasets, and Cloud Storage. 2 Build, lists Notebooks, AutoML, Training, Deep Learning VM Image, and Deep Learning Containers. 3 Validate, lists AI Explanations, What-if tool, Vizier. 4 Deploy, lists Prediction, and TensorFlow Enterprise

Resources

Pricing

There are no minimum fees or up-front commitments, and there’s no charge for using Notebooks. You pay only for the cloud resources you use with the Notebooks instance: Compute Engine, Cloud Storage, Vertex Training, Vertex Predictions' BigQuery, and others. Our pricing calculator can help you estimate the costs of running your workloads.

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