This is great news; with the CS & ML basics covered, we in the industry [1] are only left with instilling some common sense into the graduates :) In academia,

This is great news; with the CS & ML basics covered, we in the industry [1] are only left with instilling some common sense into the graduates :) In academia, it's publish or perish, so much of the cutting edge research is over-engineered (over-researched?) and too brittle to be relied on in production. Not to mention lacking a usable implementation. Because in practice, many business problems that call for automation and ML can be solved using the simplest of techniques to a satisfactory degree. The challenge fresh graduates face is rarely advanced math. It's usually solving the right problem and making the solution robust enough to be reliable in production (+communicating this to all stakeholders). Model interpretability and your ability to analyse errors and iterate the solution are worth way more than a few percent gain in accuracy (accuracy/f1 are rarely the measures most relevant to the business goals too; the cost matrix is usually trickier than that). Pulling every opaque deep learning library under the sun into a system that could be solved using a few regexps and ifs to get a 5% KPI boost is not a good idea. Building practical Machine Learning models is as much about solid engineering and understanding the business objectives as it is about math&theory, though the math cannot be skipped. We're not nearly at the stage where some "generic ML in the cloud" can cut it, wherever there's real money on the line. Successful systems are still very domain specific, built with significant SME expertise. [1] Source & plug: we run a ML mentoring program for promising university students, as well as give corporate trainings on Machine Learning in Python: http://rare-technologies.com http://rare-technologies.com