About DataSphere
Yandex DataSphere is a full-cycle ML development environment. Yandex DataSphere offers powerful features to easily work with Yandex Cloud services.
In DataSphere, you can train models and perform computations in DataSphere Notebook, run remote computations using DataSphere Jobs jobs, deploy the trained models or any Docker images as a service in DataSphere Inference.
Yandex DataSphere advantages
Ready-to-use development environment
You do not need to spend time creating and maintaining VMs: when you create a new project, computing resources are automatically allocated for implementing it.
The VM already has the pre-installed JupyterLab development environment and packages for data analysis and machine learning (TensorFlow, Torch, Keras, NumPy, etc.) on it, and you can start using them immediately. For the full list of packages, see List of pre-installed software.
If you are missing a package, you can install it right from a notebook or build a custom Docker image.
Flexible choice of computing resources
DataSphere offers a wide range of ready-made computing resource configurations. You can select one or multiple configurations and get a managed service without the need to set up a VM. The allocated resources will be assigned to you as long as you are using it or until you intentionally release the VM. By default, an idle VM is released in three hours, but you can set the time to reduce costs or to keep the selected configuration assigned to you.
Organizations and resource hierarchy
DataSphere is not just a cloud: it allows all organization members to work in a shared space managed by Yandex Cloud Organization. Resources you create depend on your projects but are not limited only to them. For more information about relationships between DataSphere resources, see Resource relationships in DataSphere.
Teamwork and cost management
We have introduced communities for you to collaborate on projects and flexibly manage your costs in DataSphere. You can link a separate Yandex Cloud billing account to each community to separate the finances of different teams. Yet communities do not isolate teams from each other and allow sharing projects and created resources.
Resource access permissions and scope are managed using roles. For more information about roles, see Access management in DataSphere.
In addition, community administrators can set up functions to be available in projects and impose limits on the use of configurations to control the costs.
Seamless use of running services
DataSphere Inference provides easy-to-use tools for deploying services based on both models trained in DataSphere and custom Dockerimages built outside DataSphere.
Aliases allow you to balance the load across multiple running nodes and publish new versions without having to stop your running service. You can create an alias in the DataSphere interface.
On the node page in the DataSphere interface, you can track the monitoring charts and logs of the deployed instances. You can also change the configuration of computing resources and send test requests to the deployed service API.
List of guides on using nodes and aliases.