MLflow VS handson-ml2

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

Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?

A text on applied data science, if you like programming and diving into datasets, this could be a good thing to have, there's a pretty good one that's free on github here.

I'd recommend a skim through the Keras/TensorFlow portion of Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow (https://github.com/ageron/handson-ml2)

Here's the GitHub repo for the 2nd Ed.

Hands-on machine learning (Python): Python reference for machine learning. Use their Github repo as a supplement because some codes in the book are outdated. Finish at least part 1: Fundamentals of machine learning.

You get access from the github, https://github.com/ageron/handson-ml2 Its free, but wont have much context without the book(also "free" at Libgen.is)

Here is the repository.

I remember an NLP course on DataCamp being helpful as an intro, but a resource I keep handy is Hands-On Machine Learning (Geron) which has really helpful follow along notebooks on the git. Then when you want some background: Deep Learning (Goodfellow)

example/18_reinforcement_learning.ipynb: This is a copy from Chapter 18 in Géron, Aurélien's book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Source code is here in GitHub.

Have you tried looking at the accompanying github repo.

Much of the material from that book is publicly available in this repo maintained by the author.