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 when faced with 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.
Kamran Yousaf explains how to substantially accelerate and radically simplify common practices in machine learning, such as running a trained model in production, to meet real-time expectations, using Redis modules that natively store and execute common models generated by Spark ML and TensorFlow algorithms. Kamran also explores the implementation of simple, real-time feed-forward neural networks with Neural Redis and outlines scenarios that can benefit from such efficient, accelerated artificial intelligence. Along the way, Kamran covers real-life implementations of these techniques at a large consumer credit company for fraud analytics, at an online ecommerce provider for user recommendations, and at a large media company for targeting content.
Kamran Yousaf is a solution architect at Redis Labs, where he specializes in the development of distributed, high-performance, low-latency architectures working with a wide range of technologies and architectures, from rule-based development, grid and low-latency applications to enterprise file sync and share. Previously, he was vice president of engineering at UK startup SME, a leader in enterprise file sync and share, and worked at GigaSpaces, BEA, and Versata.
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