Moving machine learning from practice to production

OP here, the post was really meant to invite discussion.

I would love to hear more from people deploying machine learning techniques at their projects, teams and companies.

years ago i dealt with NN's in automated trading, and little regard was given to source data requirements. it was like NN's were treated as magic boxes rather than black boxes. a magic box will miraculously produce useful output even when you feed it garbage, but a black box has specific requirements for its data source to make it useful. so i like how the author dedicates one entire part of the three-part flow chart to sources and preprocessing. if i deal with NN's again i'll definitely place more emphasis on this aspect.