Machine Learning Life Cycle — ML Technical Interview Questions & Answers

Data collection, preparation, training, evaluation and deployment.

Machine Learning Life Cycle

  • Simple meaning: An ML project is a loop, not “fit once and forget”. Collect data → clean → train → check on unseen data → (maybe) put it in an app → watch if it still works next month.
  • Picture: College result predictor. Collect last year marks. Remove blank rows. Train. Test. If test is OK, a small API guesses this year’s marks. Next year the paper pattern changes — you retrain.
  • Board steps: (1) question (what are we predicting?) (2) data (3) clean + features (4) train/test (5) metric (6) deploy (7) monitor.
  • Trap: jumping to a fancy model before the question and the metric are clear. Trap: never checking the model after it goes live.

Technical Interview Tip: Question first, then data, then train/test, then deploy and watch.

Code Examples for Machine Learning Life Cycle

Machine Learning Life Cycle — working example

# Supervised idea: learn y from X
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]  # roughly y = 2x

def predict(x, w=2.0):
    return w * x

preds = [predict(row[0]) for row in X]
mse = sum((yt - yp) ** 2 for yt, yp in zip(y, preds)) / len(y)
print(preds, mse)

Machine Learning Life Cycle — mL learns patterns from data. Start with features X, labels y, a model, and a metric like MSE/accuracy.

How to Explain Machine Learning Life Cycle to an Interviewer

Name the ML life cycle.

Collect Clean Train Evaluate Deploy Monitor

What comes before picking XGBoost?

The prediction question The metric