The common perception of machine learning is that it starts with data and ends with a model. In real-world production systems, the traditional data science and machine learning workflow of data preparation, feature engineering and model selection, while important, is only one aspect. True machine intelligence requires continuous, real-time learning by closing the feedback loop. Equally important for enterprise-grade and mission-critical systems are the typical elements of software systems, such as scalability, fault tolerance and security.
Nick is a Principal Engineer at IBM’s Spark Technology Center. He is a member of the Apache Spark PMC and author of Machine Learning with Spark. Previously, he co-founded Graphflow, a startup focused on recommendations and customer intelligence. He has worked at Goldman Sachs, Cognitive Match, and led the Data Science team at Mxit, Africa’s largest social network. He is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.