Depends on what you mean by machine learning. In deep learning applications optimizations and getting the thing to train effectively is 90% of the job. My team

Depends on what you mean by machine learning. In deep learning applications optimizations and getting the thing to train effectively is 90% of the job. My team manages a platform / framework we built similar to FloydHub (we wrote a django app that integrates with AWS, but any cloud provider would do) and another similar to SigOpts (we built a server-client system that utilizes the first systems APIs to deploy nodes)[1]. This lets us effectively develop and then hyperparameter tune our models. Finally, we deploy them within a library and flask app. This makes them easily digestible across the enterprise. We are a team of three that leverage other teams to make improvements (aka “inner sourcing” development). It’s a busy job, but managing the whole stack gives us the ability to develop models much faster and effectively. With multiple teams utilizing our frameworks we have a kind of critical mass to keep everything running quite well. [1] https://medium.com/capital-one-tech/system-language-agnostic-hyperparameter-optimization-at-scale-and-its-importance-for-automl-92d9f9add416 https://medium.com/capital-one-tech/system-language-agnostic...