Features
- Dynamic Fractional GPU Autoscaling: Efficiently scale ML models and entire GPUs up and down based on demand, with precise memory allocations for each model.
- Priority-Based Rate Limiting: Incorporates built-in request quota control with configurable priorities to prioritize enterprise customers, ensuring seamless service delivery.
- Effortless and Automatable Deployments: Containerize and publish your models effortlessly in a few simple steps. Automate deployment via REST API or use the intuitive web UI.
- Seamless Integration with Major ML Frameworks: Supports all major ML frameworks without necessitating code modifications. Simply take your model and deploy it seamlessly.
- Cloud Agnostic Capability: MLnative seamlessly installs on all major cloud solutions as well as on-premise infrastructure, providing flexibility in deployment.
- Comprehensive Control and Security: Run models within your environment, ensuring data security behind your firewalls. Features built-in security scanning, audit logs, and Single Sign-On (SSO).
Use Cases:
- Powerful Generative AI: MLnative excels in handling generative AI use cases, unleashing the potential of creative and dynamic model outputs.
- High-Performance LLM Text-to-Speech: MLnative delivers high-performance ML inference, specifically optimized for large language model (LLM) text-to-speech applications.
- Efficient Computer Vision: MLnative provides robust support for computer vision models, ensuring low latency even during periods of high traffic.
MLnative stands as the pinnacle of MLOps, offering unprecedented speed and scale. It emerges as the go-to platform for running machine learning models in production, enhancing efficiency and cost-effectiveness.
