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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.