What are the key components/stages of a typical Machine Learning pipeline?

Question

Accepted Answer

A typical Machine Learning pipeline has several stages, each critical for a successful production model. 1. Problem Framing — define the business problem, success metrics, and ML formulation (classification? regression? ranking?). 2. Data Collection — gather data from databases, logs, APIs, or external sources. 3. Data Cleaning & Preprocessing — handle missing values, outliers, encoding, scaling. 4. Feature Engineering — create informative features; do feature selection. 5. Model Selection & Training — pick algorithms, train on training data, tune hyperparameters. 6. Evaluation — validate on held-out data using appropriate metrics (accuracy, F1, AUC, RMSE). 7. Deployment — package the model and serve it (REST, batch, edge). 8. Monitoring & Maintenance — track drift, latency, and accuracy in production; retrain when needed. Walk the pipeline end-to-end. Many candidates skip Problem Framing or Monitoring — those are exactly where production ML breaks. Mentioning data versioning, drift monitoring, and feedback loops signals real production experience.