The realization that AI-driven decision-making is indispensable in todays
fast-paced and ultra-competitive marketplace has raised interest in industrial
machine learning (ML) applications significantly. The current demand for
analytics experts vastly exceeds the supply. One solution to this problem is to
increase the user-friendliness of ML frameworks to make them more accessible
for the non-expert. Automated machine learning (AutoML) is an attempt to solve
the problem of expertise by providing fully automated off-the-shelf solutions
for model choice and hyperparameter tuning. This paper analyzed the potential
of AutoML for applications within business analytics, which could help to
increase the adoption rate of ML across all industries. The H2O AutoML
framework was benchmarked against a manually tuned stacked ML model on three
real-world datasets to test its performance, robustness, and reliability. The
manually tuned ML model could reach a performance advantage in all three case
studies used in the experiment. Nevertheless, the H2O AutoML package proved to
be quite potent. It is fast, easy to use, and delivers reliable results, which
come close to a professionally tuned ML model. The H2O AutoML framework in its
current capacity is a valuable tool to support fast prototyping with the
potential to shorten development and deployment cycles. It can also bridge the
existing gap between supply and demand for ML experts and is a big step towards
fully automated decisions in business analytics.

本文分析了自动机器学习在企业分析应用中的潜力，并将 H2O AutoML 框架与手工调整的 ML 模型在三个真实数据集上进行了基准测试，发现 H2O AutoML 框架快速、易用、可靠，表现接近手工调优的 ML 模型，是一个有价值的工具，能够支持快速原型设计，缩短开发和部署周期，并能够弥合 ML 专家供需之间的差距。