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Photo by Matthew Sleeper on Unsplash

I recently participated in the Industry Speakers section of the Computing and Information Systems Doctoral Colloquium at the University of Melbourne. The idea of this segment of the day, is to give final year doctoral students the opportunity to hear from a few people who were in their shoes some time ago and chose to move away from academia into other fields of endeavour. Hopefully we would bring some complementary career path perspectives to those of the mid-career professionals they are most familiar with — their faculty advisors and other professors.

For me that pivotal moment of stepping outside the comforting familiarity of the academic system into the wide and rather intimidating world is now 12 years ago and I’ve survived and even thrived, albeit with some bumps along the way. Speaking with students after the talk, they told me that what I had shared was helpful so I wanted to offer the key points I covered to a broader audience here. …


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Photo by Fabian Grohs on Unsplash

Machine learning is all the rage and sure, it’s fun to be a tourist at ICML, drink up all the exciting new developments, come home and tinker around with the magic new techniques. However, with apologies to Thomas Edison, the life of ML in the enterprise is “1% algorithm and 99% perspiration”. And successful implementation of ‘ML inside’ products takes a team of committed professionals, not a couple of lone data scientists.

This is why most Wednesday lunch times over the last four months, you would have found me teaching a ‘Machine Learning Learning’ course to my good friends and colleagues in software engineering. I’d like to say this was altruistic behaviour on my behalf but it really wasn’t. For one thing, I throughly enjoyed it and for another, as someone fervently interested in building and deploying ML pipelines to make products more personalised, more engaging and more useful, I know my life will be a whole lot easier if more engineers understand the practical basics of the machine learning lifecycle. …

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