ML & MLOps Index

ML interviews vary wildly by company and role. The consistent theme: can you build reliable ML systems, not just train models.

Core pillars

1. Modeling fundamentals (bias/variance, metrics, evaluation)
2. Data (quality, leakage, features, labeling)
3. Production (deployment, monitoring, iteration)

Production mindset (high signal)

Two reference frames this repo uses:

• Rules of ML (engineering-first approach)
• ML Test Score (tests/monitoring rubric for production readiness)

See RESOURCES.md.

What changes in 2025–2026

• More interviews explicitly evaluate how you work with AI tools.
• LLM/GenAI systems design shows up more (retrieval, evaluation, safety, cost).
• MLOps signals matter more: monitoring, rollback, shadow deploy, data issues.

Next pages

• 05-ML-MLOPS/01-ML-Fundamentals-for-Interviews.md
• 05-ML-MLOPS/04-ML-Testing-and-ML-Test-Score.md
• 05-ML-MLOPS/06-Monitoring-and-Drift.md