The EXPLAIN project (EXPLanatory interactive Artificial intelligence for INdustry) aims at enabling explainable Machine Learning in industry. MLOps (Machine Learning Operations) includes tools, practices, and processes for deploying ML (Machine Learning) in production. These will be extended by explainability methods as part of the project.

This study aims to determine to what extent MLOps is implemented by four project partner companies. Further, the study describes the ML use cases, MLOps software architecture, tools, and requirements in the companies perspective. Besides, requirements for a novel MLOps software architecture, including explainability methods, are collected.

As a result the interviews show that each of the interviewed industry partners use MLOps differently. Different tools and architectural patterns are used depending on the particular use case. Overall, most information we gathered focused on architecture decisions in the MLOps tool landscape used by the interviewed companies.

In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately.This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. For evaluation purposes, three test scenarios with different characteristics are manually chosen.Various models are trained, optimized, and compared with each other using common performance metrics. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices.

An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.