KDnuggets™ News 21:n13, Apr 7: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Making Machine Learning Models Understandable

In this double issue of KDnuggets: Top 10 Python Libraries Data Scientists should know in 2021; KDnuggets Top Blogs Reward Program; Shapash: Making Machine Learning Models Understandable; Easy AutoML in Python; The 8 Most Common Data Scientists; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1; and much, much more.

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• Working With Time Series Using SQL, by Michael Grogan
• How to Dockerize Any Machine Learning Application, by Arunn Thevapalan
• Automated Text Classification with EvalML, by Angela Lin
• The Best Machine Learning Frameworks & Extensions for TensorFlow, by Derrick Mwiti
• How to deploy Machine Learning/Deep Learning models to the web, by Ahmad Anis
• Awesome Tricks And Best Practices From Kaggle, by Bex T.
• What’s ETL?, by Omer Mahmood
• 3 More Free Top Notch Natural Language Processing Courses, by Matthew Mayo
• Introduction to the White-Box AI: the Concept of Interpretability, by SciForce
• Software Engineering Best Practices for Data Scientists, by Madison Hunter
• Why So Many Data Scientists Quit Good Jobs at Great Companies, by Adam Sroka
• Explainable Visual Reasoning: How MIT Builds Neural Networks that can Explain Themselves, by Jesus Rodriguez
• How to break a model in 20 days — a tutorial on production model analytics, by Dral & Samuylova
• MongoDB in the Cloud: Three Solutions for 2021, by Krueger & Franklin
• Overview of MLOps, by Steve Shwartz
• Multilingual CLIP with Huggingface + PyTorch Lightning, by Sachin Abeywardana
• Extraction of Objects In Images and Videos Using 5 Lines of Code, by Ayoola Olafenwa
• Rejection Sampling with Python, by Michael Grogan

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From Overview of MLOps

Source: https://www.kdnuggets.com/2021/n13.html