Your 90-Day Study Plan

A structured approach to MLOps interview preparation. This plan assumes 2-3 hours of daily study.

Phase 1: Foundations (Days 1-30)

Focus on core infrastructure skills that appear in every MLOps interview.

Week	Focus Area	Daily Activities
1	Docker Deep Dive	Multi-stage builds, layer optimization, security scanning
2	Kubernetes Fundamentals	Pods, Deployments, Services, ConfigMaps, Secrets
3	Kubernetes Advanced	StatefulSets, GPU scheduling, HPA, Resource quotas
4	Cloud ML Services	AWS SageMaker, GCP Vertex AI, Azure ML fundamentals

Week 1-4 Checklist

# Docker skills to master
- [ ] Build multi-stage Dockerfiles for ML models
- [ ] Implement Docker layer caching for pip dependencies
- [ ] Run container security scans with Trivy
- [ ] Optimize image size (target: <500MB for inference)

# Kubernetes skills to master
- [ ] Deploy ML model with Deployment + Service
- [ ] Configure GPU node pools and tolerations
- [ ] Set up Horizontal Pod Autoscaler for model serving
- [ ] Implement liveness/readiness probes for ML containers

Phase 2: MLOps Stack (Days 31-60)

Deep dive into the tools that define MLOps engineering.

Week	Focus Area	Key Technologies
5	ML Pipelines	Kubeflow Pipelines, Airflow, Prefect
6	Model Serving	TensorFlow Serving, Triton, BentoML, Ray Serve
7	Experiment Tracking	MLflow, Weights & Biases, DVC
8	Feature Stores	Feast, Tecton concepts, feature engineering

Week 5-8 Project

Build a complete MLOps pipeline:

# Pipeline project specification
components:
  data_validation:
    tool: Great Expectations
    checks: schema, distribution, null rates

  training:
    orchestrator: Kubeflow or Airflow
    tracking: MLflow
    versioning: DVC

  serving:
    framework: BentoML or Triton
    deployment: Kubernetes
    scaling: HPA based on QPS

  monitoring:
    drift: Evidently
    metrics: Prometheus + Grafana

Phase 3: System Design & Practice (Days 61-90)

Intensive interview simulation and system design practice.

Week	Focus Area	Activities
9	System Design Fundamentals	Study common patterns, practice 2 designs/week
10	Mock Interviews	Schedule 2-3 mock interviews with peers
11	Company-Specific Prep	Research target companies, customize examples
12	Final Polish	Review weak areas, refine STAR stories

System Design Practice Problems

Practice these in order of complexity:

1. Basic: Design a batch prediction pipeline
2. Intermediate: Design a real-time feature serving system
3. Advanced: Design a multi-model A/B testing platform
4. Expert: Design infrastructure for 1M+ QPS model serving

Daily Schedule Template

# Optimal daily study structure
daily_schedule = {
    "morning_1hr": "Theory/Reading - new concepts",
    "afternoon_1hr": "Hands-on - build/code exercises",
    "evening_1hr": "Practice - mock problems, system design"
}

# Weekend intensive (4-6 hours)
weekend_focus = [
    "Complete weekly project milestone",
    "One full system design session",
    "Review and fill knowledge gaps"
]

Progress Tracking

Milestone	Target Date	Verification
Docker certification-ready	Day 14	Build 3 production Dockerfiles
Kubernetes proficient	Day 30	Deploy ML app to K8s from scratch
Pipeline builder	Day 45	Complete end-to-end pipeline project
System design confident	Day 75	Pass 2 mock design interviews
Interview ready	Day 90	Complete 3 full mock interviews

Commitment Check: If you can't commit 2-3 hours daily, extend this to a 120-day plan. Consistent practice beats cramming.

You now have a complete roadmap. The next module dives deep into Infrastructure & Deployment questions. :::