Amazing machine learning and open source tools and their functions.
I'm a software developer by day and a passionate writer and reader by heart. My love for storytelling drives me to craft immersive worlds, whether through code or prose. When I'm not lost in a book or writing, I enjoy unraveling mysteries, exploring hidden gems in literature, and watching the sky shift colors at sunset. Words and technology are my twin obsessions, each offering their own kind of magic.
There are lots of Data Science & Machine Learning tools everyone knows, like Pandas, Numpy, scikit-learn, and more. Here are some promising #MLOps tools that you should definitely check and add to your stack:
DVC by Iterative - Management, versioning of datasets, and machine learning models.
FastDS by DagsHub - a command-line wrapper around Git and DVC, meant to minimize the chances of human error, automate repetitive tasks, and provide a smoother landing for new users.
Deepchecks - Test Suites for Validating ML Models & Data.
MLFlow by Databricks - Open source platform for the machine learning lifecycle.
PyCaret - Open source, low-code machine learning library in Python.
ManiFold by Uber - A model-agnostic visual debugging tool for machine learning.
Evidently AI - Interactive reports to analyze ML models during validation or production monitoring.
CML by Iterative - Open-source library for implementing CI/CD in machine learning projects.
Are there any other data tools you feel data scientists & #ML Engineers should watch out for? Tag them and state their functions to educate others.
