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。

自动化机器学习：业务分析中的 AI 决策