Many articles and demos about device-based ML focus on vision tasks. It’s easy to be intimidated by the latest headlines about AI systems achieving superhuman performance in voice transcription or game-playing, but as Satya Nadella writes in his book, “Hit Refresh” (HarperBusiness, 2017), “Every organization today needs new cloud-based infrastructure and applications that can convert vast amounts of data into predictive and analytical power through the use of advanced analytics, machine learning, and AI.” (From Twitter: “It’s AI when you’re raising money, it’s ML when you’re hiring devs.”) As a career strategy, competence in ML technologies is one of the hottest ways toward career flexibility and higher compensation.
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CoreML on iOS and Tensorflow Android Inference on Android are straightforward and consistent once you understand the tools and workflow. While AI/ML research is advancing at a truly giddy pace, a less celebrated but equally exciting trend is the availability of easy-to-use libraries for delivering ML functionality on mobile and edge devices. Either way, the AI/ML train is leaving the station and you don’t want to be left behind. Whether it’s AlphaGo besting the best human Go player or AlphaGo Zero beating that in three days of learning the game from scratch, or Microsoft Research setting a new benchmark for conversation speech recognition, every week seems to bring some new advance built on “deep learning” and “artificial neural networks.” Or perhaps you’ve been more interested in the headlines about bidding wars on the salaries and signing bonuses for developers with ML competence. You’ve read the headlines: Artificial intelligence and machine learning (AI/ML) technologies are rewriting the benchmarks across a vast swath of hard problems. Volume 32 Number 13 Machine Learning - Deliver On-Device Machine Learning Solutions