Data science might be a young field, but that doesn’t mean you won’t face expectations about having an awareness of certain topics. This article covers several of the most important recent developments and influential thought pieces.

Topics covered in these papers range from the orchestration of the DS workflow to breakthroughs in faster neural networks to a rethinking of our fundamental approach to problem solving with statistics. For each paper, I offer ideas for how you can apply these ideas to your own work

#1 — Hidden Technical Debt in Machine Learning Systems

The team at Google Research provides clear instructions on antipatterns to avoid when setting up your data science workflow. This paper borrows the metaphor of technical debt from software engineering and applies it to data science.

As the next paper explores in greater detail, building a machine learning product is a highly specialized subset of software engineering, so it makes sense that many lessons drawn from this discipline will apply to data science as well.

How to use: follow the experts’ practical tips to streamline development and production.

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