Three things I wish I knew earlier about Machine Learning

I've been working with Machine Learning models both in academic and industrial settings for a few years now. I've recently been watching the excellent Scalable ML from Mikio Braun, this is to learn some more about Scala and Spark. His video series talks about the practicalities of'big data' and so made me think what I wish I knew earlier about Machine Learning I'll take each in turn. I gave a talk on Data-Products and getting Ordinary Differential Equations into production. One thing that I didn't realise until sometime afterwards was just how challenging it is to handle issues like model decay, evaluation of models in production, dev-ops etc all by yourself.