Some useful Data Science tool I learned from Full Stack Deep Learning


Tracking ML experiment result (Useful for DL project)


GPU version of Pandas


Check the dataset before you dive into it. (Useful for ML project)



Very similar to metaflow


  • Tracking experiments to record and compare parameters and results ().
  • Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production ().
  • Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms ().
  • Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations ().


Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production.


An open source platform to deploy your machine learning models on Kubernetes at massive scale. Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.

Machine Learning / Deep Learning / Python / Flutter

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