Machine learning (ML) has become a popular tool in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to streamline this deployment and management process. One of the remaining MLOps challenges is the need for explanations. These explanations are essential for understanding how ML models reason, which is key to trust and acceptance. Better identification of errors and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed in practice when accuracy and especially explainability do not meet user expectations. We developed a novel MLOps software architecture to address the challenge of integrating explanations and feedback capabilities into the ML development and deployment processes. In the project EXPLAIN, our architecture is implemented in a series of industrial use cases. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.

翻译：机器学习（ML）已成为工业领域广泛应用的实用工具，有助于优化运营、提升效率并降低成本。然而，在生产环境中部署和管理ML模型具有复杂性，而机器学习运维（MLOps）正是为此而生。MLOps旨在简化这一部署与管理流程。当前MLOps面临的关键挑战之一是对解释的需求。这些解释对于理解ML模型的推理机制至关重要，而后者正是建立信任与认可度的基础。更好的错误识别能力和更高的模型精度仅是其中两点优势。常被忽视的现实是：当精度尤其是可解释性无法满足用户预期时，已部署的模型在实际应用中往往会被弃用。针对如何将解释与反馈能力融入ML开发部署流程这一挑战，我们开发了一种新型MLOps软件架构。在EXPLAIN项目中，该架构已在系列工业用例中得到实施。所提出的MLOps软件架构具有多重优势：提供高效的生产环境模型管理方式，同时支持将解释机制无缝集成至开发与部署流程中。