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Sagify is a tool that helps you manage your machine learning (ML) projects on AWS SageMaker. AWS SageMaker is a service that lets you build, train and deploy ML models in the cloud. Sagify simplifies the process of using AWS SageMaker by providing a command-line interface (CLI) that hides the low-level details and lets you focus on your ML code.

With Sagify, you can train, tune and deploy hundreds of ML models by implementing just two functions: a train function and a predict function. The train function defines how to train your model on your data, and the predict function defines how to make predictions with your model. Sagify supports different types of ML frameworks, such as scikit-learn, TensorFlow, PyTorch and more.

Sagify also helps you monitor your training metrics, such as accuracy, loss, precision and recall. You can use Sagify's API to log these metrics and visualize them on AWS CloudWatch. This way, you can track the performance of your models and compare different experiments.

Sagify is an open-source project that is available for free. You can install it with pip: pip install sagify. You can also find more information and documentation on its website: https://www.sagifyml.com/ or its GitHub page: https://kenza-ai.github.io/sagify/.

Sagify is a great tool for data scientists who want to use AWS SageMaker for their ML projects. It makes MLOps (ML operations) easier and faster by automating the training and deployment of ML models. If you are looking for a data science friendly interface for AWS SageMaker, you should give Sagify a try!