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

Automated machine learning: AI-driven decision making in business analytics

Industrial machine learning systems face data challenges that are often
under-explored in the academic literature. Common data challenges are data
distribution shifts, missing values and anomalies. In this paper, we discuss
data challenges and solutions in the context of a Neural Forecasting
application on labor planning.We discuss how to make this forecasting system
resilient to these data challenges. We address changes in data distribution
with a periodic retraining scheme and discuss the critical importance of model
stability in this setting. Furthermore, we show how our deep learning model
deals with missing values natively without requiring imputation. Finally, we
describe how we detect anomalies in the input data and mitigate their effect
before they impact the forecasts. This results in a fully autonomous
forecasting system that compares favorably to a hybrid system consisting of the
algorithm and human overrides.

本文讨论了工业机器学习系统中的数据挑战及解决方案。结合劳动规划案例，介绍了如何使预测系统对数据分布变化、缺失值和异常值具有鲁棒性。采用周期性重训练策略以解决数据分布变化，着重讨论了数据稳定性的关键重要性。此外，文章还展示了深度学习模型如何在不需要插补的情况下本地化处理缺失值。最后，文章介绍了如何检测输入数据中的异常并在其影响预测之前减轻其影响。这样，我们得到了一个完全自主的预测系统，与由算法和人工控制混合的系统相比具有更好的效果。

弹性神经预测系统

Resilient Neural Forecasting Systems

In industrial machine learning pipelines, data often arrive in parts.
Particularly in the case of deep neural networks, it may be too expensive to
train the model from scratch each time, so one would rather use a previously
learned model and the new data to improve performance. However, deep neural
networks are prone to getting stuck in a suboptimal solution when trained on
only new data as compared to the full dataset. Our work focuses on a continuous
learning setup where the task is always the same and new parts of data arrive
sequentially. We apply a Bayesian approach to update the posterior
approximation with each new piece of data and find this method to outperform
the traditional approach in our experiments.

本文研究了工业机器学习中的深度神经网络在不断学习过程中优化性能的方法，使用贝叶斯方法更新后验概率，与传统方法相比实验表明可以取得更好的效果。