Explain core ML concepts and lifecycle

Q: Explain core ML concepts and lifecycle

This is a Machine Learning interview question from Capital One for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

You are interviewing for an ML Engineer role. Answer the following (conceptually; no code required):

1) Bias–variance tradeoff

What are bias and variance?
How do they relate to underfitting vs. overfitting?
Name practical levers to move along the tradeoff.

2) Parameters vs. hyperparameters

Define each and give common examples.
Who/what “sets” each (training vs. tuning)?

3) Batch inference vs. real-time inference

Contrast latency, throughput, cost, and typical use cases.
What changes in feature computation and serving architecture?

4) Training with very large datasets

If training data is extremely large (doesn’t fit on a single machine), what strategies would you use?

5) Feature engineering

What is feature engineering?
Why does it matter even with modern models?

6) Walk through an end-to-end ML lifecycle

Describe an end-to-end ML lifecycle from problem framing to deployment and iteration.
Then walk through one end-to-end project you personally delivered (problem, data, modeling, evaluation, deployment, monitoring, iteration).