ML Cloud – The first national AI/ML platform

ML Cloud is an out-of-the-box, user-friendly and functional ML engineer's workspace, deployed in an accelerated (NVIDIA GPU) AI/ML collective (Tensor Cloud) or private (HTI) cloud.

ML Cloud combines an integrated, pre-configured and self-contained collection of "best of breed" open source software for ML/AI tasks and the world's most powerful NVIDIA H100 accelerators.

The ready-to-use ML engineer workspace is a significant lowering of the entry barrier to the world of AI/ML. And the latest NVIDIA tensor accelerators provide unattainable performance for other technologies on the tasks of machine learning and productive inference.

ML Cloud – The first national AI/ML platform

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A cloud-ready platform for AI/ML

A high-performance tensor computing platform for AI/ML based on a private or collective cloud.

State-of-the-art NVIDIA H100 accelerators combined with an integrated VMware vSphere virtualization stack, VMware Tanzu Kubernetes Grid and NVIDIA AI Enterprise.

An integrated set of "best of breed" open source software as an ML engineer's working environment. De Novo ML Cloud is 2 times cheaper than the prices of hyperscalers and provides 100% predictability of costs.

De Novo ML Cloud is 2 times cheaper than the prices of hyperscalers and provides 100% predictability of costs.

ML Cloud is a proven technical solution

ML Cloud provides an opportunity

Manage the Machine Learning Lifecycle in Kubeflow

including model development (Kubeflow Notebooks), model training (Kubeflow Pipelines), model usage (KServe). Jupyter Notebook (the main tool in the data science engineer's work) can be created and configured in Kubeflow. Jupyter Notebook can be run with or without GPU access.

Use MLFlow to work with machine learning artifacts:

any files and statistics generated during training, for example: loss function plots, metric logs, sample test images, model checkpoints, optimizer checkpoints, model weights.

Download and save data with MinIO.

It is an open source tool that provides an S3-like interface for working with data.

Work with data markup for computer vision tasks in CVAT,

which integrates with MinIO in order to read datasets (videos, images) uploaded to MinIO. Markup can also be stored next to the dataset on MinIO.

Deploy solutions to markup other data types on demand,

such as a textual data markup tool.

Monitor system status with Grafana.

By default, the user gets access to a set of dashboards. In this way, you can learn about the operation of the entire cluster, the load, available and occupied resources, and much more.

More about K8s as a Service and AI/ML

Watch the video: Kubernetes as a service in De Novo clouds

In the era of container development, our specialists have created several tools for convenient creation and management of container virtualization clusters based on VMware Tanzu Kubernetes Grid in De Novo clouds.