
Yandex DataSphere
A service for ML development that combines the most popular tools and scalable resources for data analysis and the full cycle of machine learning: from an experiment to the production of a finished model.
Familiar interface
Serverless computing
Flexible choice of resources
Popular libraries
Distributed computing
Questions and answers
What is Yandex DataSphere?
DataSphere is a cloud environment that combines the most popular tools and resources for ML development. It helps you perform the full cycle of creating a model: from an experiment and development to the production of a finished version on Yandex.Cloud computing resources.
DataSphere is a cloud environment that combines the most popular tools and resources for ML development. It helps you perform the full cycle of creating a model: from an experiment and development to the production of a finished version on Yandex.Cloud computing resources.
What are the advantages of the Yandex DataSphere serverless environment?
- You don’t need to spend time configuring and maintaining VMs: when you create a new project or run calculations, computing resources are allocated automatically.
- You can scale the resources you need for data analysis and ml development within the same scenario.
- The service supports various computer configurations, including VMs with Nvidia V100 GPU and distributed computing on a SPARK cluster.
- As a development environment, the service uses the popular JupyterLab interface.
- You can start using basic packages for data analysis and machine learning immediately, including TensorFlow, Keras, NumPy, PyTorch, CatBoost, LightGBM, and more.
All this helps significantly reduce the cost of ML as compared to computing on your own hardware or other cloud platforms, as well as reduce the time for development and speed up the transfer of models from the experimental stage to commissioning.
- You don’t need to spend time configuring and maintaining VMs: when you create a new project or run calculations, computing resources are allocated automatically.
- You can scale the resources you need for data analysis and ml development within the same scenario.
- The service supports various computer configurations, including VMs with Nvidia V100 GPU and distributed computing on a SPARK cluster.
- As a development environment, the service uses the popular JupyterLab interface.
- You can start using basic packages for data analysis and machine learning immediately, including TensorFlow, Keras, NumPy, PyTorch, CatBoost, LightGBM, and more.
All this helps significantly reduce the cost of ML as compared to computing on your own hardware or other cloud platforms, as well as reduce the time for development and speed up the transfer of models from the experimental stage to commissioning.
Get started with DataSphere
Useful links
TensorFlow is a registered trademark of Google Inc.
PyTorch is a registered trademark of Facebook, Inc.