A European shipping company was looking to gain a competitive advantage by leveraging ML techniques. The aim was to create shipping-lane specific demand forecasting, and to implement it throughout its operations, in order to save time and manual labor, adjust pricing and business agreements, and utilize smart resource allocation. Each percentage of improvement is worth $1.5 million. 

In order to effectively operationalize a model you need to cross 3 chasms: 1) business relevance, 2) operationalizing models and 3) translating predictions to business impact. In this talk, Moran will explain these three elements using this real-world case study. He will highlight common mistakes when operationalizing a Machine Learning model in an enterprise environment and how to avoid them.

This talk is ideal for Data Scientists, Product Managers, Development Managers and other business stakeholders that work with Data Scientists.