Editor’s Note: This is the first part of a short 3-part series covering some of the key themes and findings from survey data featured in our recent 2020 State of the Industry Report, co-authored by Fritz AI and Spell. You can download the report for free here.

Key Finding #1: Access to ML Expertise is Inconsistent and Splintered

It’s fun and inspiring to consider all the possible use cases and transformational experiences that cutting edge technologies like mobile machine learning promise.

However, we’ve learned (through lots of exploration, trial and error, and our research) that there are some fundamental gaps to bridge between these amazing ideas and machine learning models being deployed into production on mobile.

One of the most startling gaps is a foundational one—access to machine learning expertise in the first place.

By the Data

We begin with a simple question: Do organizations with mobile development projects even have access to machine learning expertise? We found that nearly 40% of respondents have no expertise at all, either in-house or external.

This suggests either a shortage in supply of ML engineers or a chasm between a growing ecosystem of ML software tools and the human expertise needed to operate them.

Of those organizations that do have access to some kind of ML expertise, more than 3/4 noted that they primarily use either in-house talent or outsourced freelancers/consultants. Only 1/4, on the other hand, noted that they employ a fully managed 3rd part solution.

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