Charmee Patel leads Product Innovation activities at Syntasa. She has extensive experience synthesizing customer, visitor, and prospect data across multiple channels and scaling emerging big data and AI systems to handle the most demanding workloads. This experience guides her work helping clients deploy innovative ways to apply AI and Machine Learning to their marketing data and developing the next generation Marketing AI Platform.
The state of the art in productionizing machine Learning models today primarily addresses building RESTful APIs. In the Digital Ecosystem, RESTful APIs are a necessary, but not sufficient, part of the complete solution for productionizing ML models. And according to recent research by the McKinsey Global Institute, applying AI in marketing and sales has the most potential value. In the digital ecosystem, productionizing ML models at an accelerated pace becomes easy with: -Feature Store with commonly used features that is available for all data scientists -Feature Stores that distill visitor behavior is ready to use feature vectors in a semi supervised manner -Data pipeline that can support the challenging demands of the digital ecosystem to feed the Feature Store on an ongoing basis -Pipeline templates that support the challenging demands of the digital ecosystem that feed feature store, predict and distribute predictions on an ongoing basis. With these, a major electronics manufacturer was able to build and productionize a new model in 3 weeks. The use case for the model is retargeting advertising; it analyzes the behavior of website visitors and builds customized audiences of the visitors that are most likely to purchase 9 different products. Using the model, this manufacturer was able to maintain the same level of purchases with half of the retargeting media spend -increasing the efficiency of their marketing spend by 100%.