Cover; Scaling Up Machine Learning; Title; Copyright; Contents; Contributors; Preface; CHAPTER 1 Scaling Up Machine Learning: Introduction; 1.1 Machine Learning Basics; 1.2 Reasons for Scaling Up Machine Learning; 1.2.1 Large Number of Data Instances; 1.2.2 High Input Dimensionality; 1.2.3 Model and Algorithm Complexity; 1.2.4 Inference Time Constraints; 1.2.5 Prediction Cascades; 1.2.6 Model Selection and Parameter Sweeps; 1.3 Key Concepts in Parallel and Distributed Computing; 1.3.1 Data Parallelism; 1.3.2 Task Parallelism; 1.4 Platform Choices and Trade-Offs; 1.5 Thinking about Performance
Summary
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies