Introduction to ML Systems Design | Best Practices for ML System Design

At the heart of every machine learning system lies a series of interconnected components, each playing a pivotal role in transforming raw data into actionable insights. Data preprocessing is the initial step, where raw data is cleaned and prepared for analysis, addressing issues such as missing values and data normalization. Feature extraction, then, involves identifying the most relevant information from the data to feed into the ML model. The model selection phase is where the magic happens; choosing the right algorithm based on the problem at hand, be it a regression, classification, or clustering task. Finally, evaluation metrics serve as the litmus test for the model's performance, with measures like accuracy, precision, and recall guiding iterative improvements.

For example, in a spam detection system, feature extraction might focus on specific keywords or sender reputation, while model selection could lean towards logistic regression or support vector machines (SVMs) for classification tasks. The model’s effectiveness would then be measured using accuracy or F1 score to balance the precision-recall trade-off.