View profile weekly - Issue #29: How did data get so big? Organizing machine learning projects. Netflix open-sources Polynote, and more...


nibble dispatch

October 24 · Issue #29 · View online

Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at

Hello everyone,
As usual, some very interesting reading this week: A history of data, some more analysis on the limitations of BERT, Science reproducibility crisis, why treating analytics like a second-class citizen will hurt you, How to organize your machine learning projects, Netflix open-sourcing a polyglot notebook, and more…
On a side note, I’m thrilled to receive such great support from all of you. You can help us push this bigger. If you like what you read, forward this issue to a friend so that they can enjoy it as well.

How did data get so big?*
How did data get so big?*
How did data get so big?
BERT: Clever but not Smart
An Inability to Reproduce
Why treating analytics like a second-class citizen will hurt you
Organizing machine learning projects
Uncertainty Quantification in Deep Learning - inovex-Blog
Netflix open-sources Polynote: an IDE-inspired polyglot notebook
Microsoft open-sources SandDance
Learning resources
Feature Engineering - Kaggle course
Step-By-Step implementation of Bayesian Optimization in Python
Graph Theory Algorithms Tutorial
On Marginal Likelihood and Cross-Validation
End notes
Call for speakers in Paris 🇫🇷
We’re looking for speakers for community events in Paris to share good practices about operationalizing data science.
If you’re working on improving the lifecycle of data science project within your organization and want to share your experience, reply to this email so we can set something up.
Have a great week!
* Illustration by Señor Salme
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