Looking at the race to build vs. buy when ramping up internal organization AI/ML capabilities:

CB Insights AI/ML Acquisition Report

Anecdotally, I have seen many organizations underestimate how much more difficult it is to build high performing AI/ML running in production vs. the traditional software development process. This is in part due to the larger number of disciplines that need to come together to build successful production AI/ML applications.

AI/ML applications continually need to be retrained and deployed back into production. Detecting model drift, retraining and deploying back into live production is a software project in itself.

Many times, the internal processes of the organization present themselves as barriers: Getting access to data, spinning up dedicated compute for AI/ML training, access to production deployment process, security and many other similar process related obstacles. Internal processes need to change to accommodate the new requirements demanded by AI/ML applications.

I have seen a few companies go down the ‘build’ route only to realize a year or two in that it’s going to require external actions to scale internal capabilities. If you want to avoid that costly detour on your road to success, reach out to us here at Noodle.ai for your Enterprise AI needs. We’d love to talk to you.

 

 

This post originally appeared on my LinkedIn profile page.

image credit: CB Insights (report linked in post)