Explain core ML concepts and lifecycle
Company: Capital One
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
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).
Quick Answer: This question evaluates understanding of core machine learning concepts and lifecycle competencies, including bias–variance tradeoff, parameters versus hyperparameters, batch versus real-time inference, strategies for training at scale, feature engineering, and end-to-end model development.
