Have you ever ever heard of an organization that successfully integrated Machine Learning into their business processes overnight, completely transforming the best way the organization operated from sooner or later to the subsequent?
Yup, me neither!
And did you probably did you realize that almost all ML models never make it to production?
Establishing production-level systems into business processes is extremely hard. By production-level, I mean, systems which have a certain level of reliability that add value to the corporate’s top and bottom line. Embedding ML systems into organizations is just not an overnight’s job and, truthfully, Data Science and Machine Learning gets a nasty rep simply because leaders wander off in the method. Particularly, I see two kinds of mistakes when attempting to experiment with ML first:
- Incorrect expectations: This one is incredibly common and the fault lies in ML vendors. High expectations about ML and AI systems are normally brought on by folks that wish to sell those systems (or by media hype). But hear me out: every ML system has error and there’s no other way around it.
