A one liner : For the DevOps nerds, AutoDeploy allows configuration based MLOps.
For the rest : So you’re a data scientist and have the greatest model on planet earth to classify dogs and cats! :). What next? It’s a steeplearning cusrve from building your model to getting it to production. MLOps, Docker, Kubernetes, asynchronous, prometheus, logging, monitoring, versioning etc. Much more to do right before you The immediate next thoughts and tasks are
- How do you get it out to your consumer to use as a service.
- How do you monitor its use?
- How do you test your model once deployed? And it can get trickier once you have multiple versions of your model. How do you perform A/B testing?
- Can i configure custom metrics and monitor them?
- What if my data distribution changes in production – how can i monitor data drift?
- My models use different frameworks. Am i covered? … and many more.
What if you could only configure a single file and get up and running with a single command. That is what AutoDeploy is!
Read our documentation to know how to get setup and get to serving your models.
- Single Configuration file support.
- Production Deployment.
- Model Monitoring.
- Custom Metrics.
- Visual Dashboard.
- Docker Compose.
- Custom Exeption Handler.
- Pydantic Validators.
- Dynamic Database.
- Data Drift Monitoring.
- Async API Server.
- Async Model Monitoring.
- Production Architecture.
- Batch Prediction.
- Preprocess configuration.
- Posprocess configuration.