Explain core ML concepts and lifecycle

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).