Product Manager. Building Machine Learning Platforms at @Dessa
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I design and build platforms & products for Engineers & non-technical users. My approach uses two key philosophies:
- Regardless of how technical your customer is (e.g. products for engineers/data scientists) - the product needs to not only have strong utility but be delightful and fun to use. Apply the same degree of design thinking to complex technical products as you would with a consumer product. As such, I try to apply UX RnD principles to API, CLI and library design.
- Prefer a systematic hypothesis driven rapid experimentation & iteration over theory. A lot of the world and human behaviour may seem chaotic to a person who is not used to a particular system (e.g. imagine being raised in Canada and attempting to drive a car in Delhi, India), but these complex systems often have some degree of order which we may not be able to spill out just using theory. That's why experimentation is so important. Develop hypothesis --> experiment --> try a reductionist approach to understanding the behaviour if possible (Note: lots of examples of where this method doesn't work e.g. dealing in Stocks, or understanding Macroeconomics) --> iterate?
I tend to switch between engineering and business/product strategy often.
Books I recommend:
Articles / Learnings etc.
Dataset: Open Typefaces, character images & CSV
Datacouncil 2019 Talk - Building Systems to Monitor Data and Model Health in Production Systems