python_koans VS MLflow

- python

The learn to code in 2 months boot camps are largely rubbish but self teaching the basics in something like Python (I like the Koans https://github.com/gregmalcolm/python_koans as they will reach you coding, Google skills, testing, and source control) will at least tell you if you like coding.

This reminds me of the Python Koans .. I loved doing those :) https://github.com/gregmalcolm/python_koans

After that, I'd work through something interactive. Lots off choices here, but maybe something like the Python koans: https://github.com/gregmalcolm/python_koans

When I started my current job I was given a couple of weeks to learn python. (I was a ruby dev previously). I really enjoyed the Python koans (https://github.com/gregmalcolm/python_koans) as a change from just reading docs, and something that gave me some small sense of achievement

I'm learning a new language right now and I'm finding its "koans" helpful. "Koans" are a format for tutorials that take the form of a script that runs on your computer, that you gradually fix up like Stardew Valley. There are Python koans here - https://github.com/gregmalcolm/python_koans

Not exactly what you asked for but a good addition to learning. https://github.com/gregmalcolm/python_koans

github.com/gregmalcolm - Python Koans

When I was learning Ruby for a job ruby koans was helpful. Python has a version here: https://github.com/gregmalcolm/python_koans not sure of the quality though.

How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.

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.