Machine Learning Logistics: Model Management in the Real World

White Papers > Artificial Intelligence > Machine Learning Logistics: Model Management in the Real World

To succeed with deep learning or AI, you must handle the machine learning logistics well. Simply put, 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 report examines what you need for effective data and model management in real-world settings, including globally distributed cloud or on-premises systems.

Authors Ted Dunning and Ellen Friedman introduce the rendezvous architecture, an innovative design to help you handle machine-learning logistics.

This report provides a basic, non-technical view of what makes the approach work, as well as in-depth technical details. The report 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.

You will learn:

  • The issues in machine learning logistics you need to consider when designing and implementing your system
  • How the rendezvous architecture leverages streaming data, provides hot hand-off of new models, and collects diagnostic data
  • Practical tips for comparing live models, including the role of decoys, canaries and the t-digest
  • Best practices for maintaining performance after deployment

Download the new white paper from O'Reilly Media to explore the world of machine learning logistics and how careful planning can impact efficiency.

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