Considerations for Deploying Machine Learning Models in Production

A common grumble among data science or machine learning researchers or practitioners is that putting a model in production is difficult. As a result, some claim that a large percentage, 87%, of models never see the light of the day in production. "I have a model, I spent considerable time developing it on my laptop. How do I get it into our production environment? What should I consider for my ML stack and tooling?"