Despite advancements in MLOps and AutoML, ML development still remains challenging for data scientists. First, there is poor support for and limited control over optimizing and evolving ML models. Second, there is lack of efficient mechanisms for continuous evolution of ML models which would leverage the knowledge gained in previous optimizations of the same or different models. We propose an experiment-driven MLOps approach which tackles these problems. Our approach relies on the concept of an experiment, which embodies a fully controllable optimization process. It introduces full traceability and repeatability to the optimization process, allows humans to be in full control of it, and enables continuous improvement of the ML system. Importantly, it also establishes knowledge, which is carried over and built across a series of experiments and allows for improving the efficiency of experimentation over time. We demonstrate our approach through its realization and application in the ExtremeXP1 project (Horizon Europe).

翻译：尽管MLOps和AutoML取得了进展，机器学习开发对数据科学家而言仍然充满挑战。首先，对机器学习模型的优化与演进缺乏充分的支持和有限的控制能力。其次，缺乏高效的机制来实现机器学习模型的持续演进，这种机制本应能够利用先前对相同或不同模型进行优化所获得的知识。我们提出了一种实验驱动的MLOps方法来解决这些问题。我们的方法依赖于"实验"这一概念，它体现了一个完全可控的优化过程。该方法为优化过程引入了完整的可追溯性和可重复性，允许人类完全掌控该过程，并实现机器学习系统的持续改进。重要的是，它还建立了跨实验序列传承和积累的知识体系，使得实验效率能够随时间不断提升。我们通过该方法在ExtremeXP1项目（欧洲地平线计划）中的实现与应用来展示其有效性。