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Beyond Predictive Analytics : Deploying apps to production and keep them improving Some smart companies have been putting predictive application in production for decades. Still, either because of lack of sharing or lack of generality, there is still no single and obvious way to put a predictive application in production today. As a consequence, for most companies, transitioning analytics from development to production is still “the next frontier”. Behind the single word "production” lays a great number of questions like: what exactly do you put in production: data, model, code all three ? Who is responsible for maintenance and quality check over time : business, tech or both ? How can I make my predictive app continuously improve and check that it delivers the promised business value over time ? What are the best practice for maintenance and updates by the way ? Will my data scientists keep working after first development or should I lay half of them off ? etc… Let’s make a small analogy with the development of web sites in the 90’s and early 00’s : Back then, the winners where not necessarily the web sites with an amazing design, but a winner had clearly made the necessary efforts and had a robust way to put their web site reliabily in production Today, every web developper can enjoy the confort of Heroku, Amazon, Github, docker, Angular, bootstrap … and so we forget. How much time before we get the same confort for the predictive world ?

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