Embedding ML systems into production is still a hard thing to do (for most companies)
Have you ever heard of a company that successfully integrated Machine Learning into their business processes overnight, completely transforming the way the organization operated from one day to the next?
Yup, me neither!
And did you did you know that most ML models never make it to production?
Setting up production-level systems into business processes is extremely hard. By production-level, I mean, systems that have a certain level of reliability that add value to the company’s top and bottom line. Embedding ML systems into organizations is not an overnight’s job and, honestly, Data Science and Machine Learning gets a bad rep just because leaders get lost in the process. Particularly, I see two types of mistakes when trying to experiment with ML first:
- Incorrect expectations: This one is extremely common and the fault lies in ML vendors. High expectations about ML and AI systems are normally caused by people that want 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.