Benchmark-ML: Cutting the Big Data Hype

This is the most important benchmark project already done in Machine Learning. I’ll let for you the summary provided:

When I started this benchmark in March 2015, the “big data” hype was all the rage, and the fanboys wanted to do machine learning on “big data” with distributed computing (Hadoop, Spark etc.), while for the datasets most people had single-machine tools were not only good enough, but also faster, with more features and less bugs. I gave quite a few talks at conferences and meetups about these benchmarks starting 2015 and while at the beginning I had several people asking angrily about my results on Spark, by 2017 most people realized single machine tools are much better for solving most of their ML problems. While Spark is a decent tool for ETL on raw data (which often is indeed “big”), its ML libraries are totally garbage and outperformed (in training time, memory footpring and even accuracy) by much better tools by orders of magnitude. Furthermore, the increase in available RAM over the last years in servers and also in the cloud, and the fact that for machine learning one typically refines the raw data into a much smaller sized data matrix is making the mostly single-machine highly-performing tools (such as xgboost, lightgbm, VW but also h2o) the best choice for most practical applications now. The big data hype is finally over.

Github Repo