Predictive data pipelines have become essential to building engaging experiences on the web today. Whether you enjoy personalized news feeds on LinkedIn and Facebook, profit from near real-time updates to search engines and recommender systems, or benefit from near-realtime fraud detection on a lost or stolen credit card, you have come to rely on the fruits of predictive data pipelines as an end user.
Running a successful machine learning project in production takes more than a clever algorithm. In this track, the experts who built some of the most successful commercial recommendation systems, will tell us what it really takes. How do you build the architectures, data pipelines and devops best practices that help drive real-world machine learning?
