How companies can navigate the age of machine learning

[A version of this post appears on the O’Reilly Radar.]

To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models.

Over the last few years, the data community has focused on gathering and collecting data, building infrastructure for that purpose, and using data to improve decision-making. We are now seeing a surge in interest in advanced analytics and machine learning across many industry verticals.

In this post, I share slides and notes from a talk I gave this past September at Strata Data NYC offering suggestions to companies interested in adding machine learning capabilities. The information stems from conversations with practitioners, researchers, and entrepreneurs at the forefront of applying machine learning across many different problem domains.
Continue reading “How companies can navigate the age of machine learning”

Fireside chat with Ben Horowitz

I had the pleasure of interviewing Ben Horowitz on the main stage at the recent Spark summit in SFO. Ben is co-founder of one of the leading tech venture capital firms a16z, and author of one of my favorite books about entrepreneurship (“The Hard Thing About Hard Things”).

The Spark Summit had a packed lineup, so I tried to cover a wide a variety of topics in the 15 minutes allotted, including cloud computing, open source, untapped opportunities in big data, and “tech bubbles”: