The Most Crucial Component in an ML Pipeline is Invisible - Container Journal

The process of building and training machine learning models is always in the spotlight. There is a lot of talk about different Neural Network architectures, or new frameworks, facilitating the idea-to-implementation transition. Moreover, many developers are putting a lot of effort into developing tools that take care of the peripherals: data management and validation, resource management, service infrastructure, the list goes on. Despite the AI craze, most projects never make it to production. In 2015, Google published a seminal paper called the Hidden Technical Debt in Machine Learning Systems.