EasyDeepLearn
Lesson

MLOps & Data Quality

215 questions8 sections

Data drift, class imbalance, monitoring, CI/CD for ML and everything that keeps models alive in production.

Every questionFlashcardsTake the quiz

Introduction

MLOps is what turns a notebook into a system: reproducible pipelines, versioned data and models, monitoring, and safe deployments. Data quality is where most production failures actually originate — silent schema changes, drift, leakage, imbalance.

This chapter covers the pragmatic concepts you'll be asked about in senior ML and MLE interviews: how you detect drift (data vs concept), how you deploy safely (shadow, canary, A/B), and how you evaluate a model *in production*, not just offline.

The 8 sections

Each section is a short read on one subject, with every answer written out. Work through them in order, or jump to the one you are weakest on.

  1. 01Data quality & driftWhat is data drift and how do you detect it?72 questions5 easy34 medium33 hard
  2. 02Features & pipelinesWhat problem does a feature store solve?24 questions1 easy20 medium3 hard
  3. 03Deployment & experimentationWhat is training-serving skew?35 questions2 easy19 medium14 hard
  4. 04Monitoring & observabilityDistributed tracing for ML inference — why?7 questions6 medium1 hard
  5. 05Reproducibility & versioningWhat is data / model lineage and why does it matter?21 questions1 easy15 medium5 hard
  6. 06Infrastructure & servingRay for ML — what does it provide?34 questions20 medium14 hard
  7. 07LLMOps & advancedHow does DP-SGD work?17 questions9 medium8 hard
  8. 08MLOps foundationsWhat does CI/CD look like for ML?5 questions1 easy4 medium