Real-World Machine Learning

Chapter 9. Scaling machine-learning workflows

This chapter covers

• Determining when to scale up workflows for model accuracy and prediction throughput
• Avoiding unnecessary investments in complex scaling strategies and heavy infrastructure
• Ways to scale linear ML algorithms to large amounts of training data
• Approaches to scaling nonlinear ML algorithms—usually a much greater challenge
• Decreasing latency and increasing throughput of predictions

In real-world machine-learning applications, scalability is often a primary concern. Many ML-based systems are required to quickly crunch new data and produce predictions, because the predictions become useless after a few milliseconds (for instance, think of real-time applications such as the stock ...