Building machine learning architecture

Creating an ML pipeline consists of multiple phases and requires iterative improvement. Building a robust and scalable workflow from a loose collection of code is a complex and time-consuming process, and many data scientists don't have experience building workflows. An ML workflow can be defined as an orchestrated sequence that involves multiple steps. Data scientists and ML developers first need to package numerous code recipes and then specify the order they should execute, keeping track of code, data, and model parameter dependencies between each step.

Added complexity to ML workflows warrants monitoring changes in data used for training and predictions because changes in the data could introduce bias, leading to inaccurate predictions. In addition to monitoring the data, data scientists and ML developers also need to monitor model predictions to ensure they are accurate and don't become skewed toward particular results over...