Sagify is a command-line utility that simplifies and expedites machine learning pipelines by providing a way to train and deploy models on AWS SageMaker in a few simple steps. It eliminates the need for configuring cloud instances, infrastructure pain, and handing over models to software engineers for deployment. Sagify offers commands for local and cloud-based training, hyperparameter optimization, and deployment. It also provides monitoring of ML models in production through integration with Superwise and Aporia. Sagify supports custom training and deployment and offers a no-code deployment option for Hugging Face models.
Sagify Features
- Simplified ML Pipelines: Train, tune, and deploy machine learning models on AWS SageMaker quickly and efficiently
- No More Configuring Cloud Instances: Implement a train function and Sagify takes care of the rest
- Hyperparameter Optimization: Implement a train function and provide a path to a JSON file containing hyperparameter ranges
- Model Deployment: Implement a predict function and Sagify handles model deployment infrastructure
- Custom Training and Deployment: Easily integrate Sagify with your own machine learning projects
- Local Testing: Test your training and prediction code locally before deploying to the cloud
- Docker Integration: Build and manage Docker images containing your machine learning codebase
- AWS SageMaker Integration: Seamlessly train, deploy, and manage models on AWS SageMaker
- Monitor ML Models in Production: Integrate with Superwise and Aporia for monitoring and maintaining deployed models
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