47. Goku Mohandas - Industry research and how to show off your projects
Towards Data Science
08/19/20
•50m
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Project-building is the single most important activity that you can get up to if you’re trying to keep your machine learning skills sharp or break into data science. But a project won’t do you much good unless you can show it off effectively and get feedback to iterate on it — and until recently, there weren’t many places you could turn to to do that.
A recent open-source initiative called MadeWithML is trying to change that, by creating an easily shareable repository of crowdsourced data science and machine learning projects, and its founder, former Apple ML researcher and startup founder Goku Mohandas, sat down with me for this episode of the TDS podcast to discuss data science projects, his experiences doing research in industry, and the MadeWithML project.
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46. Ihab Ilyas - Data cleaning is finally being automated
August 12, 2020
•39m
It’s cliché to say that data cleaning accounts for 80% of a data scientist’s job, but it’s directionally true.
That’s too bad, because fun things like data exploration, visualization and modelling are the reason most people get into data science. So it’s a good thing that there’s a major push underway in industry to automate data cleaning as much as possible.
One of the leaders of that effort is Ihab Ilyas, a professor at the University of Waterloo and founder of two companies, Tamr and Inductiv, both of which are focused on the early stages of the data science lifecycle: data cleaning and data integration. Ihab knows an awful lot about data cleaning and data engineering, and has some really great insights to share about the future direction of the space — including what work is left for data scientists, once you automate away data cleaning.
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Data science is about much more than jupyter notebooks, because data science problems are about more than machine learning.
What data should I collect? How good does my model need to be to be “good enough” to solve my problem? What form should my project take for it to be useful? Should it be a dashboard, a live app, or something else entirely? How do I deploy it? How do I make sure something awful and unexpected doesn’t happen when it’s deployed in production?
None of these questions can be answered by importing sklearn and pandas and hacking away in a jupyter notebook. Data science problems take a unique combination of business savvy and software engineering know-how, and that’s why Emmanuel Ameisen wrote a book called Building Machine Learning Powered Applications: Going from Idea to Product. Emmanuel is a machine learning engineer at Stripe, and formerly worked as Head of AI at Insight Data Science, where he oversaw the development of dozens of machine learning products.
Our conversation was focused on the missing links in most online data science education: business instinct, data exploration, model evaluation and deployment.
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