There are barriers and complications to successful real-world implementation of machine learning projects. This presentation will outline practical solutions to overcoming them including: selling the benefits to the business; collaborating with data engineers to deliver supporting data infrastructure; learning from, and working with developers to put models into production; building testing into the process and future-proofing ongoing maintenance of the solution.

Resource invested in these areas ultimately generates higher return than time spent building the perfect machine learning model.