When data scientists are done building their analytical models, there are questions to ask:
- How do the model results get to the hands of the decision makers or applications that benefit from this analysis?
- Can the model run automatically without issues and how does it recover from failure?
- What happens if the model becomes stale because it was trained on data that is no longer relevant?
- How do you deploy and manage new versions of that model without breaking downstream consumers?
This talk aims to illustrate the importance of these questions and provide a perspective on how to address them. We will share our experiences deploying models at our clients and some of the problems we have encountered along the way, along with some best practices and coding examples.