Brokyl supports any machine learning models created with Python files or Jupyter notebooks, including those using popular libraries like TensorFlow, PyTorch, scikit-learn, and more.

Each update to your model repository is automatically deployed to a new version. You can manage and roll back to previous versions via the Brokyl dashboard.

Brokyl automatically adjusts the computational resources allocated to your model based on current demand, ensuring optimal performance without manual intervention.

Brokyl aims to keep latency low, typically within milliseconds to a few seconds, depending on model complexity and current load.

Brokyl uses industry-standard security measures, including encryption in transit and at rest, secure API keys for access control, and regular security audits.

Sign up on the Brokyl website, connect your GitHub repository or upload your model files, and follow the on-screen instructions to deploy your model.

Yes, you can book a demo through the Brokyl website or contact their sales team at hello@brokyl.com for more information.