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Welcome to Nebius AI Cloud
Nebius Academy
Overview
This video introduces the Nebius AI Cloud platform, explaining its structure and how different components work together. It covers the authentication process, the home dashboard for an overview of resources, and the crucial concepts of projects and regions for environment management. The summary then delves into core services, including compute (VMs, Slurm, Kubernetes), storage (object, file systems), AI services (jobs, endpoints, MLflow, Ray, SkyPilot), and applications (pre-packaged tools). Finally, it touches upon the administrative section for billing, auditing, and access management, emphasizing how these elements support the entire AI workload lifecycle.
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Chapters
- Users authenticate via Google, GitHub, Microsoft, or SSO for quick individual access or enterprise integration.
- The home dashboard provides a live overview of all resources within the current project.
- The dashboard features sections for 'My Resources,' 'Features' (common starting points like Kubernetes), and 'Workloads' (running jobs).
Understanding the initial login and dashboard layout is essential for navigating the platform and quickly accessing your resources and common tools.
Logging in using an SSO account to access the main dashboard.
- Projects act as isolation boundaries to separate environments, teams, or different types of workloads (e.g., experimentation vs. production).
- Switching projects changes the entire context of visible resources, as every resource belongs to a specific project.
- Regions define the physical location where infrastructure runs, impacting resource availability and quotas.
- All Nebius Cloud resources are scoped by both a project and a region.
Properly understanding projects and regions is critical for organizing your work, managing resource deployment locations, and ensuring data locality and compliance.
Using separate projects for development and production environments to prevent accidental interference.
- Open Compute provides the infrastructure layer, allowing the creation of Virtual Machines (VMs) with full control over CPU/GPU, storage, and networking.
- VMs are the most flexible building blocks for general-purpose computing.
- SoOperator offers managed Slurm clusters for high-performance, multi-node distributed workloads, handling orchestration automatically.
- Managed Kubernetes provides a control plane for orchestrating containerized applications, including GPU-enabled node groups.
These services form the foundation for running any workload, offering varying levels of control and abstraction for different computational needs.
Provisioning a GPU-enabled VM to train a machine learning model.
- Storage services include object storage, shared file systems, and other persistent options.
- Object storage is ideal for datasets, model checkpoints, and artifacts.
- Shared file systems enable simultaneous access to the same storage by multiple machines.
- Storage is a fundamental component underpinning almost every workload on the platform.
Reliable and accessible storage is crucial for managing data, model artifacts, and ensuring that compute resources can access the necessary files.
Storing large training datasets in object storage for easy access by multiple compute instances.
- AI services offer higher-level abstractions for AI workloads, built on top of the infrastructure.
- Jobs allow running training, fine-tuning, or batch tasks without manual VM provisioning.
- Endpoints deploy trained models as APIs for real-time inference, enabling production services.
- Tools like MLflow (experiment tracking), Ray (distributed Python), and SkyPilot (distributed AI job management) are integrated.
These services streamline the AI development lifecycle, allowing users to focus on model development and deployment rather than infrastructure management.
Deploying a trained image recognition model as an API endpoint for an application.
- The Applications section allows deploying pre-packaged tools and frameworks.
- These ready-to-use environments significantly reduce setup time and accelerate common workflows.
- Users can launch complex setups in just a few steps.
Leveraging pre-configured applications speeds up development and experimentation by providing instant access to common tools and environments.
Launching a pre-configured Kubernetes cluster for a web application with a few clicks.
- The manage section provides visibility and control over the entire environment.
- Billing allows monitoring usage and costs.
- Administration and audit logs track user activity and system changes.
- Identity and Access Management (IAM) handles user permissions and access control.
This layer is essential for maintaining security, accountability, and cost-effectiveness across your Nebius Cloud usage.
Reviewing billing reports to understand the cost breakdown of different services used.
Key takeaways
- Nebius Cloud organizes resources using a hierarchical structure of Projects and Regions to provide isolation and control over deployment locations.
- The platform offers a spectrum of compute services, from flexible VMs to managed Slurm and Kubernetes clusters, catering to diverse workload needs.
- Integrated AI services abstract away infrastructure complexity, enabling faster model training, fine-tuning, and deployment.
- Pre-packaged Applications significantly reduce the time required to set up common development and deployment environments.
- Effective management of storage is fundamental, with options like object storage and shared file systems supporting various data needs.
- The administrative section is crucial for governance, providing tools for cost management, security auditing, and access control.
- Nebius Cloud aims to support the complete lifecycle of AI workloads, from initial experimentation to production deployment.
Key terms
Nebius AI CloudProjectsRegionsVirtual Machines (VMs)SoOperatorKubernetesObject StorageShared File SystemsAI JobsEndpointsMLflowRaySkyPilotApplicationsIdentity and Access Management (IAM)
Test your understanding
- How do Projects and Regions work together to define your cloud environment in Nebius?
- What are the primary differences between using Virtual Machines, SoOperator, and Kubernetes for compute tasks?
- Explain the purpose of AI Services like Jobs and Endpoints in the context of a machine learning workflow.
- Why is the administrative section, including billing and IAM, as important as the core compute and AI services?
- How can the 'Applications' feature help accelerate the development and deployment process?