Here’s what data scientists love about Modzy
Easy Model Deployment
- Quickly wrap your model code into a Docker container that meets the Modzy API specifications
- Upload your own models to your private instance of Modzy using our self-service model upload feature
- Deploy your model, or one of the many pre-trained models from the model marketplace into production in your preferred environment– cloud, on-premise, or to an edge device
- Use Modzy to monitor your models’ performance and how AI is being used within your organization
- Monitor job completion status through API key and job management features
- Keep track of model drift to identify when it’s time to retrain models
- Easily select and specify required resources necessary for model deployment
- Auto-scaling for managing and controlling infrastructure costs
- Ensemble Modzy’s patent-pending data scanning model with others you’ve deployed to flag any poisoned data before it’s fed through your models
- Harden your models by retraining with Modzy’s patent-pending robust training method to keep them secure against threats
Resources for Data Scientists
With a few lines of code, Modzy allows data scientists to quickly package and deploy models into production at enterprise scale. Upload models you’ve already trained, or save time by choosing pre-trained or retrainable models from the Modzy model marketplace that meet your needs. Once the models have been deployed, Modzy allows you to track and monitor performance in real-time, so that you can intervene when a model displays drift or isn’t performing as expected. Modzy moves you past last-mile challenges that come with deploying AI models into production, saving you time and frustration, all while allowing you to deploy trusted AI at scale.
What is Robust Training for Adversarial Defense?
Listen to Dr. Arash Rahnama describe how Modzy robust training enhances AI model security.