MLnative

What is MLnative?

MLnative is a platform designed for running Machine Learning models in production, offering significant improvements in resource utilization and cost efficiency. It features GPU sharing, autoscaling, customizable priority queues, easy deployments, and a user-friendly interface for managing ML models. The platform can be deployed on both cloud resources and on-premise infrastructure, providing control over the environment.

Key features of MLnative include GPU sharing, autoscaling, customizable priority queues, easy deployments, and support for web apps and REST APIs. It leverages a mix of open-source technologies and proprietary optimizations to maximize GPU utilization and scalability.

MLnative's infrastructure is fully isolated, ensuring that no data leaves the company network. The platform offers dedicated support, detailed documentation, example integrations, and a dedicated support channel for customers. Additionally, it supports air-gapped environments for enhanced security measures.

If you have further questions or need more information about MLnative, you can schedule a meeting with their team to discuss how the platform can meet your specific requirements.

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.