Comparing Mobile Machine Learning Frameworks

Important Editor's Note: Heartbeat is sponsored by Fritz, one of the platforms covered in this post. The author of this post was paid for this content (we pay all contributors for all content); however, all information, research, and perspectives included are solely the author's and do not include any editorial input/control from Heartbeat or Fritz In the past few years we've seen many startups and even mature companies coming up with new mobile apps or features powered by machine learning and AI. These features require some heavy, real-time processing by neural networks. Data roundtrips for inference, the cost of backend servers to support millions of devices, concerns surrounding user data privacy. But luckily, there's a way to solve these issues: Mobile Machine Learning.