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

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

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Machine Learning | Deep Learning | https://linktr.ee/yanwei

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Yanwei Liu

Yanwei Liu

Machine Learning | Deep Learning | https://linktr.ee/yanwei

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