Python Cheatsheet VS MLflow

It appears that it's not an actual "sheet"; but, a crowd-sourced book. From the GitHub page, if you click on https://www.pythoncheatsheet.org/ and scroll to the bottom of the page and click on "Contributing," you go to the page on how to contribute to the "book." Subsequent "pages," accessed from the lower right corner take you to "Basics" and Built-In Functions" and more. It's not a sheet,

I like the site: www.pythoncheatsheet.org for people coming from a programming background that just want to go over the basics and easy things like "what is the syntax for a dictionary".

You can replace that udemy course with these: https://www.pythoncheatsheet.org/ https://www.gormanalysis.com/blog/python-numpy-for-your-grandma/ https://www.gormanalysis.com/blog/python-pandas-for-your-grandpa/ https://www.gormanalysis.com/blog/neural-networks-for-your-dog/

Trace each line of code and ensure that it functions as intended. You may explore how they function and learn more about the syntax by using python cheat sheet. You can also use trace table to trace the program and understand the conditions. Therefore, discuss each line of code and write a brief and concise comment about it.

i always have to pull up a "cheat sheet" as i do my classwork. the one i've been using for python is https://www.pythoncheatsheet.org/ it clearly defines operators, data types, and is super helpful when starting out.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

MLFlow - http://mlflow.org

MLflow:

In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.

Hi, you should have a look at ML flow https://mlflow.org or weight and biases https://wandb.ai/site