The Foundation for the
Intelligent Enterprise

Enterprises are incorporating Artificial Intelligence (AI) into their business applications in order to efficiently process the vast amounts of important data they are collecting. AI brings great power, but also many challenges. KuberLab accelerates AI adoption and solves the challenges involved with a new solution for Enterprise AI application development and deployment.

KuberLab Enables AI Application
Development from Research to Production

KuberLab's platform for Enterprise AI development and deployment can be implemented in the cloud or on-premise. This platform contains all the tools and automation required for the AI workflow from inception to operation.

We provide an extensive catalog of pre-trained models and templates that can be used as a starting point for AI application development.

Our solution facilitates collaboration between data science, development, and operations teams by allowing them to work together in the same environment regardless of local or cloud deployments.

Product

AI APPLICATIONS
  • Use a catalog of commonly used applications or easily add your own
  • Choose automatic or manual deployment and update
  • Manage application lifecycle for different environments (Development, QA, Staging, Production)
  • Use GIT for version control

Flexible Infrastructure Management
  • Automatic creation and update of clusters on public cloud:
    • AWS
    • Google Cloud
    • Azure
  • Support for custom bare-metal installations
  • Infrastructure monitoring tools
  • Single pane of management for all clusters

Security
  • User applications have an additional layer of isolation
  • Configurable access model for containers in the cluster
  • Role-based cluster management
  • Support for multi-tenancy

Models
  • Catalog of pre-built AI application templates
  • AI applications that support various frameworks
  • On-demand, elastic resource allocation
  • Real-time monitoring of model execution: logs, metrics and resources
  • Model version management
  • Inference (start serving as a microservice on different clusters), and scaling
  • Data source integration

AI Application Development and Deployment Workflow

  • 1. Data Acquisition and Processing
    • Connectors to multiple data sources
    • NFS storage
    • Support of popular data management tools
  • 2. Model Design
    • Integration with data science IDEs and libraries
    • Support of all major ML frameworks
    • Enterprise template catalogue
    • Collaboration workspace
  • 3. Model Training
    • Distributed training
    • Optimized for h/w accelerators
    • Resource/job scheduling
    • Infrastructure monitoring
  • 4. Evaluation
    • Detailed metrics dashboard
    • Model versioning
    • Model benchmarking
  • 5. Integration
    • One-click serving
    • Integration with existing CI/CD process
  • 6. Deployment
    • Support of multiple target h/w (servers, mobile, embedded)
  • 7. Monitoring
    • Collecting performance metrics on live data
    • Triggering model retraining based on schedule/events
  • Full support of hybrid cloud
  • Access polices
  • Resource/application orchestration
  • LDAP integration
  • Built-in workflow engine
  • Private repository

Multi-Cloud Elastic Environment

The KuberLab Platform deployed in the cloud, connecting the enterprise to local clusters, or clusters in the Public Cloud, or a combination of both