While many companies aim to use Machine Learning (ML) models, transitioning to deployment and practical application of such models can be very time-consuming and technically challenging. To address this, MLOps (ML Operations) offers processes, tools, practices, and patterns to bring ML models into operation. A large number of tools and platforms have been created to support architects and developers in creating practical solutions. However, specific needs vary strongly in a situation-dependent manner, and a good overview of their characteristics is missing, making the architect’s task very challenging.
We conducted a systematic literature review (SLR) to identify key features, patterns, and platform characteristics. As a result, we provide an overview of the technical design space of MLOps and insights into different ML platforms. Our review can help architects select MLOps components and support them in their development efforts.

Machine Learning Operations (MLOps) involves software development practices for Machine Learning (ML), including data management, preprocessing, model training, deployment, and monitoring. While MLOps have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud-based. This article addresses this shortcoming based on our and our partner’s real industrial experience in various projects. While there is a broad range of challenges for MLOps in cyber-physical production systems (CPPS), we focus on those related to data, models, and operations as we assume these will directly benefit the reader and provide solutions such as lightweight integration, integration of domain knowledge, periodic calibration, and interactive interfaces. In this way, we want to support practitioners in setting up
industrial MLOps environments in CPPS. Further, we discuss explainability as an additional part of MLOps, which should be explored in more detail in the future.

While many companies aim to use Machine Learning (ML) models, transitioning to deployment and practical application of such models can be very time-consuming and technically challenging. To address this, MLOps (ML Operations) offers processes, tools, practices, and patterns to bring ML models into operation. A large number of tools and platforms have been created to support developers in creating practical solutions. However, specific needs vary strongly in a situation-dependent manner, and a good overview of their characteristics is missing, making the architect’s task very challenging. We conducted a systematic literature review (SLR) of MLOps platforms, describing their qualities, features, tactics, and patterns. In this paper, we want to map the design space of MLOps platforms. We are guided by the Attribute-Driven Design (ADD) methodology. In this way, we want to
provide software architects with a tool to support their work in the platform area.

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 capabilitiesand flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collectionof practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed inthe context of pure software systems, Industry 4.0 systems received much less attention. So far, there is only little researchfocusing on MLOps for Industry 4.0. In this paper, we discuss whether MLOps in Industry 4.0 leads to significantly dif-ferent challenges compared to typical Internet systems. We provide an initial analysis of MLOps approaches and identifyboth context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specificchallenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicatesthat existing tools and approaches are also mostly suited for the Industry 4.0 context.

MLOps have become an increasingly important topic in the deployment of machine learning in production. While Machine Learning Operations was predominantly used as a buzzword for methods in Machine Learning (ML) for the time being, since 2019, they are increasingly used in the context of deploying ML algorithms. This report is a protocol for a systematic literature review (SLR) that aims to determine the MLOps terminology and identify related activities. A further goal of the SLR is to identify where MLOps can be linked to classical software engineering. In addition, related automation techniques are considered.

The projected literature review aims to draw conclusions from papers that explicitly use the term MLOps or Machine Learning Operations with the objective to provide the necessary common baseline for future MLOps research and practice. This report thoroughly documents the SLR method, processes, and data material. We also gathered all relevant data to comprehend MLOps fully. Through our comprehensive analysis, we hope to
provide valuable insights and recommendations for optimizing MLOps practices.

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.