Design principles for machine learning architecture

Designing an effective ML architecture requires a strategic approach, prioritizing scalability, maintainability, efficiency, and robustness. Here are some design principles that professionals typically adhere to when developing ML architectures.

Organizing the machine learning system into modules

Modularity breaks down the ML system into separate, interchangeable components or modules, each responsible for a distinct function. In an ML model, for instance, you could have one module for data ingestion, another for preprocessing, one for model training, and yet another for prediction serving. Consider a retail recommendation system: the data ingestion module might be responsible for collecting user interaction and purchase history, while another module uses that data to train a model that recommends products. The advantage is that if a better recommendation algorithm is developed, the training module can be replaced or updated...