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).




Machine Learning | Deep Learning |

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

2 Design Principles I Personally Use for iOS Projects

Find Near & Around places based on your location: HMS Site, Location, Map Kit

PHP or Which is Better for Enterprise

Then I joined the Docker class by him and the Workshop on Artificial intelligence and Machine…

#freestockphoto (Friday 20th 08AM)

Functional Programming and first class functions

“Experience creating a high level architecture and how it helps to see an overall picture of the…

A Framework for Mobile Product Strategy and Development

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Yanwei Liu

Yanwei Liu

Machine Learning | Deep Learning |

More from Medium

Predicting Rain with Machine Learning

Getting Started with Machine Learning and Python.

How to Build a Machine Learning Web App in Python Using Gradio

Top 8 Most Important Unsupervised Machine Learning Algorithms With Python Code References