Predictive intelligence from machine learning has the potential to change everything in our day to day experiences, from education to entertainment, from travel to healthcare, from business to leisure and everything in between. Modern ML frameworks are batch by nature and cannot pivot on the fly to changing user data or situations. Many simple ML applications such as those that enhance the user experience, can benefit from real-time robust predictive models that adapt on the fly.
Join this session to learn how common practices in machine learning such as running a trained model in production can be substantially accelerated and radically simplified by using Redis modules that natively store and execute common models generated by Spark ML and Tensorflow algorithms. We will also discuss the implementation of simple, real-time feed-forward neural networks with Neural Redis and scenarios that can benefit from such efficient, accelerated artificial intelligence.
Real-life implementations of these new techniques at a large consumer credit company for fraud analytics, at an online e-commerce provider for user recommendations and at a large media company for targeting content will also be discussed. #SFdd9
Cihan is a big data enthusiast who brings over twenty years of experience to Redis Labs’ product team. Cihan started his career as a C/C++ developer. Most recently, Cihan was Dir. of Product Management at Couchbase, responsible for the Couchbase Server product. Cihan was also part of the team in Redmond, WA that launched Azure public cloud platform at Microsoft. He also delivered a number of versions of SQL Server product suite. Previously Cihan worked on Informix and Illustra database products that were later acquired by IBM.
Cihan holds a number of patents in the database field, and has a Bachelor of Science in Computer Engineering and a Master of Science in Database Systems.