In this talk, we will cover the journey we undertook to go from a fully outsourced model to over a dozen internally-built Machine Learning models deployed in production that are ROI positive and that solve real business problems, in about two years. We will discuss challenges faced along the way, key design methodologies and technologies utilized, experimentation approaches, and how we established a culture of constant learning, iteration and improvement. We will also look at how we brought business stakeholders along the journey while ensuring the Data Science team delivered quick wins to gain credibility and simultaneously built the foundation to improve productivity.
