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.