02-experiment-tracking

2. Experiment tracking and model management

• Slides

2.1 Experiment tracking intro

2.2 Getting started with MLflow

Note: in the videos, Cristian uses Jupyter in VS code and runs everything locally

But if you set up a VM in the previous module, you can keep using it and use the usual Jupyter from your browser. There's no significant difference between using Jupyter with VS code and without

2.3 Experiment tracking with MLflow

2.4 Model management

2.5 Model registry

Starting MLflow 2.9, model registry stages are deprecated. Please use model version tags and aliases instead of stages. For example, instead of transition_model_version_stage(name, version, stage) use set_registered_model_alias(name, alias, version). More details here and here.

2.6 MLflow in practice

2.7 MLflow: benefits, limitations and alternatives

2.7 Homework

More information here.

Notes

Did you take notes? Add them here:

• Notes/General Docs on MLflow by Ayoub
• Minimalist MLflow code reference by Anna V
• Notes from second lesson by Neimv
• 2nd Week Experiment & Tracking notes by Ayoub.B
• Experiment tracking (jupyterbook) by particle1331
• Week 2: Experiment & Tracking Notes by Bengsoon Chuah
• 2.4 Model Management Notes by Alvaro Pena
• Notes by Alvaro Navas
• Notebook from froukje and notes
• Blog post on setting up MLFlow on GCP by Isaac Kargar.
• Week2: Experiment tracking notes and notebook by Bhagabat
• Notes of ML-flow by Jaime Cabrera-Salcedo
• Experiment tracking with MLflow by Hongfan (Amber)
• Running MLflow with Docker and on Minikube
• Chapter 2: Full Notes by Marcus
• Experiemnt Tracking and Mlflow by Annaliese Tech
• Adding Hyperparameter Tuning to Your Notebook with MLflow and Hyperopt by Annaliese Tech
• Send a PR, add your notes above this line