Key Concepts for Deploying Machine Learning Models to Mobile

By Spell - 2020-12-02

Description

In this guest article from Fritz AI, we'll be outlining the key considerations in building a mobile-focused ML pipeline from end-to-end.

Summary

Introduction The worlds of mobile development and machine learning are quite far apart.
For example, a perfect set of images scraped from the web leads to trained models that likely perform poorly in the real world.
Similarly, a technique known as knowledge distillation can be used to train much smaller networks that mimic the results of larger, more accurate ones.
For instance, if your model is supposed to analyze video in real-time (i.e.
runs at 30 FPS on all target mobile devices), there are a number of steps you’ll need to take in terms of monitoring and managing your workflow.

Topics

Machine_Learning (0.21)
Backend (0.2)
NLP (0.16)

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