MLnative

What is MLnative?

Who created MLnative?

MLnative was founded by Łukasz and Tomek. Łukasz leads product development, with over 10 years of software engineering experience and previous experience as an Ex-DataRobot Tech Lead. Tomek focuses on non-technical areas, with 8+ years of experience and a background as an Ex-Big4 Consultant. The company is based in Poland but operates globally, offering a platform for running machine learning models efficiently in production environments.

What is MLnative used for?

• Real-time inferences
• Models use GPUs
• High peaks in demand
• Many models in production
• No time for downtimes
• Custom ML models
• Flexible configuration
• Data must stay in your network
• Generative AI
• LLM

How to use MLnative?

To use MLnative for running machine learning models in production, follow these steps:

1. Platform Deployment: MLnative can be deployed on cloud resources or on-premise infrastructure to keep everything under control.

2. Key Features:

   • GPU Sharing
   • Autoscaling
   • Customizable Priority Queues
   • Easy Deployments via Web app and REST API

3. Functionality:

   • MLnative provides a dedicated platform with an intuitive UI and programming APIs for managing models in production.
   • Leverages open-source technologies and proprietary tweaks for maximizing GPU utilization and scalability.

4. Security:

   • Clusters are fully isolated, ensuring no communication with external services and data never leaves your servers.
   • Regular security scanning, SSO with RBAC support, and audit logs for enhanced security.

5. Support:

   • Comprehensive documentation, end-to-end example integrations, and a dedicated support channel for onboarding assistance.
   • Active support during the initial onboarding phase ensures a smooth transition.

6. Air-Gapped Environments: MLnative supports air-gapped environments, providing installation packages and guidance for effective usage in demanding security scenarios.

7. Easy Deployment:

   • Containerize and publish models easily via REST API or web UI, with automated deployment options.
   • Cloud-agnostic, allowing installation on major cloud solutions and on-premise infrastructure.

8. Monitoring and Control:

   • Run models in your environment to ensure data remains secure within your firewalls.
   • Built-in security scanning, audit logs, and SSO for enhanced control and monitoring.

9. Get Started:

   • Play with foundational models on the AI Playground or via API to experience the speed and efficiency of ML models on the platform.
   • Book a meeting with the MLnative team for personalized assistance and insights.

By following these steps, you can effectively utilize the advanced features and capabilities of MLnative for your machine learning model deployment requirements.