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：数据爆炸及其在过去几年中日益复杂的程度,使得最低运作率和最低运作率系统更容易发生故障,需要在这些系统中嵌入新的工具以避免这种故障。 在这一演示中,我们将在最低运作率系统的可观察性模块中引入关键工具,该模块针对数据调节和模型版本控制等难题,以优化模型选择。 我们认为,将这些特征纳入我们的最低运作率和最低运作率系统管道将大大有助于建立一个能避免早期最低运作率系统故障的强大系统。