View profile weekly - Issue #20: A.I. hype, ML pipelines, history and future of ML...


nibble dispatch

June 27 · Issue #20 · 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

Our top pick this week is The BS-Industrial Complex of Phony A.I. by Mike Mallazzo, a former employee at Dynamic Yield, recently acquired by McDonald’s.
For the last few years, startups have shamelessly re-branded rudimentary machine-learning algorithms as the dawn of the singularity, aided by investors and analysts who have a vested interest in building up the hype. Welcome to the artificial intelligence bullshit-industrial complex.

The valley is banking on A.I. to be its next god and if there is no god, it becomes necessary to invent him
The valley is banking on A.I. to be its next god and if there is no god, it becomes necessary to invent him
History and future of Machine Learning 🎙
How have we gotten to this point with machine learning? And where are we going?
An interview with one of the OG researchers and teachers of machine learning, Professor Tom Mitchell of Carnegie Mellon University.
End-to-end machine learning pipelines in real-world applications 🎙
Overcoming challenges in productionizing machine learning models.
An interview with Nick Pentreath, principal engineer at IBM, focusing on building open source tools that enable end-to-end machine learning pipelines. The conversation spans many topics, including a Python library for adversarial attacks and defenses, model zoos, tools for model developments, governance and operations, etc.
The growing recognition that ML is sufficiently different from traditional software that you need specialized tools
The promise and perils of artificial intelligence in biology
Machine learning methods are powerful, but they’re very easy to abuse, in particular applied to technical fields like microbiology. In this interview, microbiologist Nick Loman talks about the promise and perils of artificial intelligence in biology.
[…] many scientists have plunged ahead with using AI before really understanding its benefits—and limitations.
So it’s the same issues that we have with statistics, but it’s a much bigger tool to shoot your foot off with, if you like. You can build these models from anything. It’s just your classic garbage in, garbage out situation.
Data-As-A-Service Bible
Everything You Wanted To Know About Running DaaS Companies
A business analysis on how to build and run data businesses from Auren Hoffmanm, CEO at SafeGraph.
Dask Release 2.0
New major version number because of a few broken APIs to improve maintainability and no more support for Python 2.
Python Visual Studio Code june 2019 release
  • Plot Viewer with the Python window
  • Parallel tests with pytest
  • Indentation of run selection in the terminal
If you’re using VSCode for data science, you will be pleased :)
Learning resources
Deep dive into CatBoost functionalities for model interpretation
CatBoost comes packed with a lot of functionalities for model interpretation, this article by Alvira Swalin, data scientist at Uber, offers a good overview of what’s possible using the library.
An overview of proxy-label approaches for semi-supervised learning
Semi-supervised learning has the wind in its sails, in particular it made an appearance in Amazon’s annual letter to shareholders.
In this post, Sebastian Ruder, research scientist at DeepMind, discusses semi-supervised learning algorithms that learn from proxy labels assigned to unlabelled data.
How to setup MLflow in production
A simple getting started tutorial for MLflow in production.
Train TensorFlow models on YARN
A quick tutorial to train TensorFlow models on YARN by Criteo AI Labs using tf-yarn, an open-source tool they’ve built.
Did we miss any good stuff? Tell us at [email protected]
Have a good week! 👋
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