The explosion of data and its ever increasing complexity in the last few years, has made MLOps systems more prone to failure, and new tools need to be embedded in such systems to avoid such failure. In this demo, we will introduce crucial tools in the observability module of a MLOps system that target difficult issues like data drfit and model version control for optimum model selection. We believe integrating these features in our MLOps pipeline would go a long way in building a robust system immune to early stage ML system failures.

翻译：近年来，数据量的激增及其复杂性的不断提升，使得MLOps系统更容易出现故障，因此需要在该类系统中嵌入新的工具以避免此类故障。在本演示中，我们将介绍MLOps系统可观测性模块中的关键工具，这些工具针对数据漂移和模型版本控制等难题，以实现最优模型选择。我们认为，将这些特性集成到我们的MLOps流水线中，将大大有助于构建一个能够抵御早期机器学习系统故障的稳健系统。