A model deployment framework for all kinds of ML models
- Deploy models built in the frameworks of your choice
- Automate the model containerization process at runtime
- Support for models written in multiple languages (Python, R, Scala, etc.)
- Vendor agnostic
Continuously serve prediction results to your applications
- The REST API endpoint for interactive requests
- Dynamically consume the best model from multiple models based on traffic behavior
- Determine model performance through A/B testing
- Safely promote and demote models
- Fallback strategies when models are not responsive
- Test models in a production environment before deployment
Healthy
0
Unhealthy
0
Min. Instances
1
Max. Instances
12
We'll take care of your infrastructure management
- Auto-scaling based on model traffic
- System monitoring dashboard for DevOps
- Alerting and health checks
Score your models on large datasets
- Offline batch scoring
- Pull and join data from multiple sources
- Feed data to your models and store their outputs
