Myelin

Myelin is an advanced, Kubernetes-native end-to-end machine learning framework designed specifically for automating MLOps processes for data scientists and machine learning engineers. It facilitates the entire lifecycle of machine learning models, allowing users to train, deploy, and monitor their models with high efficiency and minimal manual intervention.

### Key Functionalities
- Self-Healing Deployments: Myelin ensures that deployments automatically recover and restore model performance without requiring human intervention, thus providing reliability in production.
- Automated Deployment Management: Streamline the process of deploying machine learning models with Myelin's automated management features that handle the complex aspects of deployment procedures.
- Declarative Architecture: Users can define the desired state of their deployments, making it easier to manage and scale applications.
- AutoML and Hyperparameter Tuning: Myelin includes integrated tools for automated machine learning processes and hyperparameter tuning to optimize model performance.
- Distributed Training and Infrastructure Provisioning: The framework supports distributed training and cloud-native infrastructure provisioning, catering to a variety of computational needs.

Myelin is particularly well-suited for complex scenarios where maintaining model integrity and performance is crucial. It offers tiered pricing plans for developers, startups, and enterprise users, with options for support and model optimization. Interested users can request a demo or obtain a free license to explore its functionalities further.