How to Benchmark Classical Machine Learning Workloads on Google Cloud

Classical machine learning algorithms like random forests and gradient boosting often outperform deep learning on the structured tabular data common in many business applications. For these CPU-centric workloads, you can systematically benchmark performance and cost on cloud platforms like Google Cloud to find the most efficient setup. Achieving reliable results involves crucial technical steps, such as managing Non-uniform Memory Access (NUMA) to ensure you are testing the hardware itself and not the system's scheduler. Using open-source suites like `scikit-learn_bench` provides a framework for running these experiments, helping you make evidence-based decisions on the best algorithms and infrastructure for your needs.