Keeping up with data — Week 43 reading list

Languages spoken around the world. Source: Why You Should Do NLP Beyond English

A really productive week is ending. Some doors are closing; new ones are opening. I got a lot of inspiration from articles I’ve read (mostly about MLops) and webinars I’ve attended (e.g. Keboola’s data science workbench). News is still full of Covid-19 second wave and US presidential election.

So please enjoy your Saturday breakfast with my reading list about data.

  1. Google’s 43 rules to help build solid data-powered products. Sound engineering principles for machine learning.
  2. Daniel Jeffries’ overview of stack covering Machine Learning lifecycle. From data gathering, model training and deployment to monitoring.
  3. a16z’s incredible insight into architecture of modern data infrastructure compiled from discussions with 20+ leading practitioners.
  4. Point Nine’s advisor Michael Wolfe on a “cadence” or “heartbeat” of best executing companies.
  5. Doug Laney’s article about challenges of predicting future in uncertain times. Trend-based, driver-based models and scenario planning.

Recently, I’m very interested in automated data quality testing of ML models in production. So I’m curious to find what’s out there. But first, let’s enjoy some more cycling outdoors before the winter comes.

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Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | Interim CDO at DataDiligence.com

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Adam Votava

Adam Votava

Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist | Interim CDO at DataDiligence.com

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