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）已成为工业领域中的热门工具，有助于改善运营、提升效率并降低成本。然而，在生产环境中部署和管理ML模型可能十分复杂。机器学习运维（MLOps）正是针对这一问题而提出的，旨在简化部署和管理流程。当前MLOps面临的挑战之一是缺乏可解释性。这些解释对于理解ML模型的推理机制至关重要，是实现信任和接受度的关键。更好的错误识别和更高的模型准确性仅是其中两个优势。一个经常被忽视的事实是，在实际应用中，当准确性和可解释性未能满足用户期望时，已部署的模型往往会被绕过。我们开发了一种新颖的MLOps软件架构，以应对将解释和反馈能力集成到ML开发与部署过程中的挑战。在EXPLAIN项目中，我们的架构已在多个工业用例中得到实施。所提出的MLOps软件架构具有若干优势：它提供了一种在生产环境中高效管理ML模型的方法，同时允许将解释功能融入开发和部署流程中。