In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next going from recommendations to products that can produce optimal strategies for meeting concrete business objectives.
We already know how to build these they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.
Jeremy Howard is an Australian data scientist and entrepreneur. He began his career in management consulting, at McKinsey & Company and AT Kearney. Howard went on to co-found FastMail in 1999 and Optimal Decisions Group. He later joined Kaggle, an online community for data scientists, as President and Chief Scientist.
Together with Rachel Thomas, he is the co-founder of fast.ai, a research institute dedicated to make Deep Learning more accessible. Previously, he was the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California.
Very quick read but the content was light and not that satisfying. Some decent high level stuff on the "Drive Train Approach" but overall I would only pick this up if you are looking for a quick read while your kids are playing at the park.
A very short book on why data science needs to be integrated with optimization techniques, to deliver data products that don't just give predictions, but act on them, and potentially transform / disrupt industries.