Learn from the DagsHub professionals what MLflow is and why it should be part of your MLOps tool kit. Gain hands-on experience using it to track experiments, register models, and deploy them to AWS.
In this session, Shambhavi covers the following topics:
1) Intro to MLflow - Learn what MLflow is and how it can help you manage your machine learning project.
2) Experiment Tracking - live logging of parameters, metrics, and artifacts as part of machine learning experiments.
3) Hands-on experience using MLflow Tracking!
📚 Additional Materials:
The Colab Notebook with the theoretical information about MLflow and MLflow Tracking demo - colab.research.google.com/dri...
The Mario vs. Wario project that we used for the demo - dagshub.com/nirbarazida/mario...
Time-stamps
00:00 - Meet & Great
03:13 - Intro + Signup
07:08 - Agenda
07:54 - Why do we need MLflow
10:52 - What is MLflow
15:40 - MLflow Tracking Functionality
19:30 - How and where runs are recorded?
25:30 - Hands-on experience using MLflow Tracking!
If you want to hear more about what we are doing at DagsHub, here are some interesting links for you:
🌐 Our Website: dagshub.com
📖 Our Blog: dagshub.com/blog/
🥰 We welcome you to join our community on Discord: / discord
Social Links:
🔗 LinkedIn: / dagshub
🐥 Twitter: / therealdagshub
DAGs out 🤙🏼