August 19, 2020
Data volumes and the complexity of machine learning continue to grow at an exponential pace, leading to huge gains in methods of storage, insight generation, and predictive modeling. However, using the same methods to capture, clean, store, and create predictions using more and more data has reached diminishing returns. Luckily, decision optimization has grown into a new prescriptive analytics field and provides a step change by enabling platforms to ingest disparate information at scale and then automatically optimize themselves to the benefit of business’ core KPIs.
Reinforcement learning solutions allow for rapid, automated improvements in business’ decisions but are currently limited from understanding the full context of those decisions. Deep learning and AI provide new avenues in this space by illuminating many hidden features about the decisions being made. For example, deep learning can uncover comprehensive similarities and differences between various advertisements so that future advertising campaigns can be made optimally with complete information.
This presentation will set up the background of predictive and prescriptive analytics, explain how deep learning builds on this field, and showcase how these solutions impact Samsung’s customers.
Matt Fritz leads the data science capability at Samsung Electronics America. His team bridges the gap between the latest research in data science and practical applications in order to keep Samsung’s marketing, operations, and digital teams on the cutting edge of machine learning. Although developing novel solutions is critical for his team, he is ultimately judged solely on the measureable financial impact of what actually gets applied by the business. His latest focus involves merging deep learning capabilities into existing prescriptive analytics solutions.
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