How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.

To succeed with machine learning or deep learning, you need an effective management system for overall data flow and the evaluation and deployment of multiple models as they move from prototype to production. Without that, your project will most likely fail. This ebook examines what you need for effective data and model management in real-world settings, including globally distributed cloud or on-premises systems. This ebook is ideal for data scientists, architects, developers, ops teams, and project managers--whether your team is planning to build a machine learning system, or currently has one underway.

Book will be released 26 Sept 2017: Visit MapR booth for free book signings or to talk about logistics 44.