50+ days of Machine Learning

 Hello :) Today is Day 135! A quick summary of today: covered module 2 of the mlops-zoomcamp by DataTalks club about experiment tracking and model management cut 2 more videos for the Scottish dataset project Firstly, about using mlflow for MLOps Maybe this is because I am starting to learn about MLOps for the 1st time and I don't know other tools, but WOW mlflow is amazing. Below are my notes from the module 2 lectures. Full code on my github repo. First, some important concepts ML experiment: the process of building an ML model experiment run: each trial in an ML experiment run artifact: any file that is associated with an ML run experiment metadata What's experiment tracking? the process of keeping track of all the relevant info from an ML experiment (could include source code, environment, data, model, hyperparams, metrics, other - these can vary) Why is experiment tracking important? reproducability organization optimization Why can't we just use an excel spreadsheet