MLOps Engineer: 8-Week Prep Path
Reading time: ~45 min | Interview relevance: Critical | Roles: MLOps Engineer, ML Platform Engineer, ML Infrastructure Engineer, ML DevOps
The Real Interview Moment
The interviewer pulls up a whiteboard and says: "Our data scientists push models to production using Jupyter notebooks and SCP. Retraining is manual. There is no monitoring. There are three models in production and twelve more in development. Design the ML platform we need."
This is the MLOps interview in a nutshell. You are not being asked to build a model -- you are being asked to build the entire ecosystem that makes models reliable, reproducible, and scalable. The MLOps Engineer is the person who turns a data scientist's prototype into a production system that serves millions of users without breaking at 3 AM.
It is a role that demands extraordinary breadth. You need to understand enough ML to know what you are operationalizing, enough DevOps to build the infrastructure, enough data engineering to manage the pipelines, and enough software engineering to make it all maintainable.
This 8-week plan covers it all.
Role Overview
What MLOps Engineers Do
MLOps Engineers build and maintain the infrastructure that makes ML work in production. They:
- Design and operate ML training and serving pipelines
- Build CI/CD systems for model development and deployment
- Implement model monitoring, alerting, and automated retraining
- Manage feature stores, model registries, and experiment tracking
- Optimize compute costs and resource utilization
- Ensure reproducibility, compliance, and governance of ML systems
| Round | Duration | Focus |
|---|
| Phone Screen | 45-60 min | Coding + infrastructure concepts |
| Coding Round | 45-60 min | Python, scripting, automation |
| System Design 1 | 60 min | ML pipeline architecture |
| System Design 2 | 60 min | Infrastructure and platform design |
| ML Fundamentals | 45 min | Enough ML to understand what you are operationalizing |
| Behavioral | 45-60 min | Reliability, incidents, collaboration |
Focus Area Allocation
Breakdown by Skill
System Design (35% -- ~70 hours total)
- ML pipeline design: training, evaluation, deployment pipelines
- Infrastructure: Kubernetes, Docker, cloud ML services
- Monitoring: model performance, data drift, system health
- CI/CD for ML: model testing, validation gates, rollback strategies
- Feature stores: online/offline architecture, consistency
- Model serving: batch vs real-time, A/B testing, canary deployments
Coding (25% -- ~50 hours total)
- Python: scripting, automation, data processing
- Infrastructure-as-code: Terraform, Helm, Docker Compose
- Pipeline tools: Airflow, Kubeflow, Prefect
- Testing: unit tests, integration tests, model validation tests
ML Fundamentals (20% -- ~40 hours total)
- Model lifecycle: training, evaluation, deployment, monitoring
- Common model types: enough to understand what you are serving
- Evaluation metrics: accuracy, precision, recall, AUC and when they matter
- Feature engineering: understanding feature pipelines
Behavioral (20% -- ~40 hours total)
- Incident response and postmortem stories
- Cross-team collaboration (DS, SWE, product)
- Reliability engineering and SLA stories
- Cost optimization achievements
8-Week Schedule Overview
Week 1: Foundations -- Coding and Containerization
Goal: Sharpen coding skills and master Docker and container fundamentals.
Daily time: 3.5 hours (weekdays), 5.5 hours (weekends)
Monday -- Python and Scripting
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode easy/medium (arrays, strings) |
| Lunch (20 min) | Read | Coding Interviews overview |
| Evening (90 min) | Study | Python scripting patterns: file I/O, subprocess, argparse, logging, config management |
| Night (15 min) | Review | Write a CLI tool that processes a CSV file |
Tuesday -- Data Structures for Infrastructure
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium (hash maps, queues) |
| Lunch (20 min) | Read | Queues, stacks, and event-driven patterns |
| Evening (90 min) | Study | Trees, graphs, topological sort (relevant for DAG-based pipelines) |
| Night (15 min) | Review | Implement topological sort for a dependency graph |
Wednesday -- Docker Deep Dive
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Docker best practices for ML |
| Evening (90 min) | Study | Dockerfile patterns, multi-stage builds, layer caching, Docker Compose, volumes, networking |
| Night (15 min) | Practice | Write a Dockerfile for an ML model serving application |
:::tip Docker Knowledge is Non-Negotiable
Every MLOps interview will test Docker knowledge. You should be able to:
- Write optimized Dockerfiles from memory
- Explain multi-stage builds and why they matter for ML images (model weights + code separation)
- Debug container networking issues
- Explain volume mounts vs bind mounts for model artifacts
:::
Thursday -- Docker Advanced and Registry
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Container registries and image management |
| Evening (90 min) | Study | Docker layer optimization, security scanning, GPU containers (NVIDIA Docker), image tagging strategies |
| Night (15 min) | Practice | Optimize a Dockerfile to reduce image size by 50% |
Friday -- Kubernetes Fundamentals
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | K8s architecture overview |
| Evening (90 min) | Study | Pods, Deployments, Services, ConfigMaps, Secrets, namespaces, resource limits |
| Night (15 min) | Review | Write a Deployment YAML for an ML model server |
Saturday -- Kubernetes for ML
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Study | GPU scheduling in K8s, node affinity, tolerations, spot instances, autoscaling (HPA, VPA, cluster autoscaler) |
| Afternoon (2 hrs) | Practice | Design a K8s cluster for ML workloads: training jobs, serving endpoints, batch processing |
| Evening (1 hr) | Review | Compare K8s deployment strategies: rolling update, blue-green, canary |
Sunday -- Week 1 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Revisit all coding problems; focus on ones you struggled with |
| Afternoon (2.5 hrs) | Practice | Write complete Docker + K8s manifests for an ML serving system |
| Evening (1 hr) | Plan | Review Week 2 plan; update resume with MLOps projects |
:::note Week 1 Milestone Checkpoint
Week 2: Foundations -- Cloud Services and Infrastructure-as-Code
Goal: Master cloud ML services and infrastructure-as-code patterns.
Daily time: 3.5 hours (weekdays), 5.5 hours (weekends)
Monday -- Cloud ML Services Overview
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | AWS SageMaker vs GCP Vertex AI vs Azure ML comparison |
| Evening (90 min) | Study | Managed training (SageMaker, Vertex AI), managed serving, batch transform, model registry services |
| Night (15 min) | Review | Create a cloud services comparison matrix |
Tuesday -- Object Storage and Data Services
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | S3/GCS patterns for ML artifacts |
| Evening (90 min) | Study | Object storage patterns, data versioning, artifact management, data lakes for ML |
| Night (15 min) | Review | Design a model artifact storage strategy |
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium (system design elements) |
| Lunch (20 min) | Read | Terraform for ML infrastructure |
| Evening (90 min) | Study | Terraform basics: providers, resources, modules, state management, workspaces |
| Night (15 min) | Practice | Write Terraform for a K8s cluster + S3 bucket + IAM roles |
Thursday -- Networking and Security
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | VPC and networking for ML |
| Evening (90 min) | Study | VPC design, security groups, IAM policies, secrets management, service mesh for ML |
| Night (15 min) | Review | Design a network architecture for an ML platform |
Friday -- ML Fundamentals: The Basics
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | ML Fundamentals overview |
| Evening (90 min) | Study | Supervised vs unsupervised learning, model types overview, training process, evaluation basics |
| Night (15 min) | Review | List the ML model lifecycle stages |
:::warning Know ML Well Enough to Be Dangerous
You do not need to derive gradient descent math, but you must understand:
- Why a model might need retraining (data drift, concept drift)
- What evaluation metrics mean and why they matter
- How training data quality affects model performance
- The difference between batch and online learning
:::
Saturday -- Compute Management
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Study | GPU management, spot/preemptible instances, cost optimization, auto-scaling ML workloads |
| Afternoon (2 hrs) | Practice | Design a cost-optimized compute strategy for training and serving |
| Evening (1 hr) | Mock | First coding mock interview (45 min) |
Sunday -- Week 2 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Revisit cloud services and IaC concepts |
| Afternoon (2.5 hrs) | Practice | Design a complete ML platform architecture on a cloud provider |
| Evening (1 hr) | Study | Read about ML platform architectures at top companies (Uber Michelangelo, Spotify ML) |
:::note Week 2 Milestone Checkpoint
Week 3: Core Infrastructure -- ML Pipelines and CI/CD
Goal: Master ML pipeline design and CI/CD for ML systems.
Daily time: 4 hours (weekdays), 6 hours (weekends)
Monday -- ML Pipeline Frameworks
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | ML System Design overview |
| Evening (120 min) | Study | Airflow, Kubeflow Pipelines, Prefect, Dagster -- architecture and trade-offs |
| Night (15 min) | Review | Compare pipeline frameworks in a decision matrix |
Tuesday -- Training Pipelines
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Training pipeline patterns |
| Evening (120 min) | Study | Data validation, preprocessing, training, evaluation, model packaging -- as pipeline steps |
| Night (15 min) | Practice | Design a training pipeline DAG |
Wednesday -- CI/CD for ML
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | CI/CD for ML best practices |
| Evening (120 min) | Study | Model testing (unit, integration, performance), validation gates, automated retraining triggers, GitOps for ML |
| Night (15 min) | Review | Design a CI/CD pipeline for model deployment |
:::tip The Three Types of ML Tests
MLOps interviews will ask about testing. Know these well:
- Data tests: Schema validation, statistical tests, distribution checks
- Model tests: Accuracy thresholds, latency benchmarks, fairness checks
- Infrastructure tests: Endpoint health, throughput, error rates
:::
Thursday -- Experiment Tracking and Model Registry
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | MLflow vs Weights & Biases vs Neptune |
| Evening (120 min) | Study | Experiment tracking: metrics, parameters, artifacts. Model registry: versioning, staging, approval workflows |
| Night (15 min) | Review | Design a model registry workflow |
Friday -- Data Validation and Quality
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Great Expectations, TFX Data Validation |
| Evening (120 min) | Study | Data quality checks, schema enforcement, data contracts, anomaly detection in data pipelines |
| Night (15 min) | Review | Write 10 data validation rules for a user behavior dataset |
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Practice | Design an end-to-end ML platform for a mid-size company (50 models, 10 data scientists) |
| Afternoon (2 hrs) | Study | ML platforms at scale: Netflix, LinkedIn, Uber case studies |
| Evening (1.5 hrs) | Mock | System design mock: design a model training pipeline (45 min) |
Sunday -- Week 3 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Summarize pipeline frameworks and CI/CD patterns |
| Afternoon (2.5 hrs) | Practice | Implement a simple ML pipeline using Airflow or Prefect concepts |
| Evening (1.5 hrs) | ML study | Model evaluation: metrics, cross-validation, stratified sampling |
:::note Week 3 Milestone Checkpoint
Week 4: Core Infrastructure -- Model Serving and ML Fundamentals
Goal: Master model serving patterns and deepen ML knowledge.
Daily time: 4 hours (weekdays), 6 hours (weekends)
Monday -- Model Serving Architectures
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Serving frameworks comparison |
| Evening (120 min) | Study | TFServing, TorchServe, Triton, BentoML, Seldon Core -- architecture and capabilities |
| Night (15 min) | Review | Compare serving frameworks feature-by-feature |
Tuesday -- Batch vs Real-Time Serving
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Batch prediction patterns |
| Evening (120 min) | Study | When to use batch vs real-time, pre-compute patterns, caching, model warm-up, cold start |
| Night (15 min) | Review | Decision tree for batch vs real-time serving |
Wednesday -- Deployment Strategies for ML
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Progressive delivery for ML models |
| Evening (120 min) | Study | Canary deployments, shadow mode, A/B testing, blue-green deployments, feature flags for models |
| Night (15 min) | Review | Design a safe model rollout strategy |
:::danger Shadow Mode is Your Best Friend
When deploying a new model, shadow mode (running the new model alongside the old one, comparing outputs without serving the new model's results) is the safest approach. Be ready to explain this in interviews and when you would use shadow mode vs canary deployments.
:::
Thursday -- ML Fundamentals: Deep Learning Basics
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Deep Learning overview |
| Evening (120 min) | Study | Neural networks, backpropagation (high level), CNNs, RNNs, transformers -- enough to understand what you are serving |
| Night (15 min) | Review | List model types and their serving requirements (latency, memory, GPU needs) |
Friday -- ML Fundamentals: Model Optimization
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Model compression techniques |
| Evening (120 min) | Study | Quantization, pruning, distillation, ONNX conversion, TensorRT optimization |
| Night (15 min) | Review | Calculate memory savings from different quantization levels |
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Practice | Design a model serving platform: multi-model, auto-scaling, health checks, rollback |
| Afternoon (2 hrs) | Study | Load balancing for ML, traffic shaping, request batching, GPU sharing |
| Evening (1.5 hrs) | Mock | System design mock: design a real-time recommendation serving system (45 min) |
Sunday -- Week 4 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Revisit all serving patterns and deployment strategies |
| Afternoon (2.5 hrs) | Practice | Design a rollback strategy for a failed model deployment |
| Evening (1.5 hrs) | Behavioral | Draft STAR stories for 3 infrastructure/reliability projects |
:::note Week 4 Milestone Checkpoint
Week 5: Advanced -- Monitoring, Feature Stores, and LLMOps
Goal: Master ML monitoring systems, feature stores, and LLM-specific operations.
Daily time: 4 hours (weekdays), 6 hours (weekends)
Monday -- ML Monitoring: Data Drift
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium/hard |
| Lunch (20 min) | Read | Data drift detection methods |
| Evening (120 min) | Study | Covariate shift, prior probability shift, concept drift, statistical tests (KS test, PSI, chi-squared) |
| Night (15 min) | Review | Implement a simple drift detection check |
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium/hard |
| Lunch (20 min) | Read | Model monitoring best practices |
| Evening (120 min) | Study | Online evaluation metrics, delayed feedback, proxy metrics, alerting thresholds, dashboards |
| Night (15 min) | Review | Design a monitoring dashboard for a recommendation model |
Wednesday -- Alerting and Incident Response
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | SRE practices for ML systems |
| Evening (120 min) | Study | Alert fatigue, escalation policies, runbooks for ML incidents, automated remediation, postmortem culture |
| Night (15 min) | Review | Write a runbook for "model latency has increased by 3x" |
Thursday -- Feature Stores
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Feast, Tecton, Hopsworks comparison |
| Evening (120 min) | Study | Online vs offline stores, feature computation, consistency, point-in-time correctness, feature sharing |
| Night (15 min) | Review | Design a feature store architecture |
Friday -- LLMOps: Operationalizing LLMs
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | LLM Interviews operations section |
| Evening (120 min) | Study | LLM serving (vLLM, TGI), prompt management, cost monitoring, quality evaluation, guardrails |
| Night (15 min) | Review | Compare LLMOps challenges vs traditional MLOps |
:::tip LLMOps is the Newest MLOps Frontier
More and more MLOps interviews include LLM-specific questions. Be ready to discuss:
- How LLM serving differs from traditional model serving (autoregressive, KV cache)
- Prompt versioning and management
- Cost monitoring and optimization for token-based billing
- Quality evaluation when ground truth labels do not exist
- Guardrails and content safety infrastructure
:::
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Practice | Design a monitoring platform: data drift, model performance, system health, alerting, dashboards |
| Afternoon (2 hrs) | Study | Observability for ML: logging, tracing, metrics. OpenTelemetry for ML |
| Evening (1.5 hrs) | Mock | System design mock: design a feature store (45 min) |
Sunday -- Week 5 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Create summary sheets for monitoring and feature stores |
| Afternoon (2.5 hrs) | Practice | Design an automated retraining pipeline triggered by drift detection |
| Evening (1.5 hrs) | Behavioral | Add 2 more STAR stories focused on incident response and cost optimization |
:::note Week 5 Milestone Checkpoint
Week 6: Advanced -- Scale, Cost, and Governance
Goal: Master scaling ML systems, cost optimization, and ML governance.
Daily time: 4 hours (weekdays), 6 hours (weekends)
Monday -- Distributed Training Infrastructure
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium/hard |
| Lunch (20 min) | Read | Data parallelism and model parallelism |
| Evening (120 min) | Study | Distributed training: data parallel, model parallel, pipeline parallel. Horovod, DeepSpeed, FSDP infrastructure |
| Night (15 min) | Review | Design a distributed training cluster |
Tuesday -- Cost Optimization at Scale
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Cloud cost optimization strategies |
| Evening (120 min) | Study | Spot instances for training, reserved instances for serving, right-sizing, GPU sharing, mixed precision savings |
| Night (15 min) | Review | Calculate cost savings for specific optimization scenarios |
Wednesday -- ML Governance and Compliance
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | ML governance frameworks |
| Evening (120 min) | Study | Model lineage, audit trails, reproducibility, fairness monitoring, regulatory compliance (GDPR, AI Act) |
| Night (15 min) | Review | Design a model governance workflow |
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Platform engineering for ML |
| Evening (120 min) | Study | Multi-tenant ML platforms, resource isolation, quota management, self-service interfaces for data scientists |
| Night (15 min) | Review | Design a self-service ML platform interface |
Friday -- Edge and Embedded ML
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Edge ML deployment patterns |
| Evening (120 min) | Study | Model deployment to edge devices, TensorFlow Lite, ONNX Runtime Mobile, model updates over-the-air |
| Night (15 min) | Review | Compare cloud vs edge deployment trade-offs |
Saturday -- Comprehensive System Design Day
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Practice | Design a complete ML platform from scratch: compute, storage, pipelines, serving, monitoring, governance |
| Afternoon (2 hrs) | Mock | System design mock: design an ML platform for autonomous vehicles (60 min) |
| Evening (1.5 hrs) | Review | Identify gaps from mock feedback |
Sunday -- Week 6 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Create comprehensive system design cheat sheet |
| Afternoon (2.5 hrs) | Practice | Solve 5 MLOps scenario questions (e.g., "Training job OOM at 80% completion -- what do you do?") |
| Evening (1.5 hrs) | Behavioral | Practice all STAR stories aloud; time them |
:::note Week 6 Milestone Checkpoint
Week 7: Polish -- Company Prep, Behavioral, and Intensive Mocks
Goal: Tailor preparation to target companies and perfect behavioral responses.
Daily time: 4 hours (weekdays), 6 hours (weekends)
Monday -- Company Research
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium (company-tagged) |
| Lunch (20 min) | Read | Company Guides |
| Evening (120 min) | Research | Target company's ML infrastructure, blog posts, tech talks, open-source tools |
| Night (15 min) | Notes | List company-specific challenges and how you would address them |
Tuesday -- Company-Specific System Design
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 company-tagged problems |
| Lunch (20 min) | Read | Company engineering blog |
| Evening (120 min) | Practice | Design a system relevant to target company's domain |
| Night (15 min) | Review | Refine design with company-specific constraints |
Wednesday -- Behavioral Deep Dive
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 LeetCode medium |
| Lunch (20 min) | Read | Behavioral guide |
| Evening (120 min) | Practice | Prepare 8 STAR stories: reliability incident, cost saving, cross-team project, failure, leadership, technical decision, mentoring, automation |
| Night (15 min) | Review | Rate each story on impact and specificity |
Thursday -- Mock Interview Day
| Time | Activity | Details |
|---|
| Morning (60 min) | Warm-up | 1 LeetCode medium |
| Afternoon (3 hrs) | Mocks | Coding mock (45 min) + system design mock (60 min) + behavioral mock (30 min) |
| Evening (30 min) | Debrief | Catalog weaknesses |
| Time | Activity | Details |
|---|
| Morning (60 min) | Study | Deep dive into weakest area from mock |
| Lunch (20 min) | Read | Related handbook chapters |
| Evening (120 min) | Practice | Targeted practice on weak areas |
| Night (15 min) | Review | Verify improvement |
Saturday -- Full Loop Simulation
| Time | Activity | Details |
|---|
| Morning (2.5 hrs) | Mock | Full interview simulation: coding + system design 1 + system design 2 |
| Afternoon (2 hrs) | Mock | Behavioral mock + ML fundamentals rapid-fire |
| Evening (1.5 hrs) | Debrief | Comprehensive feedback review |
Sunday -- Week 7 Review
| Time | Activity | Details |
|---|
| Morning (2 hrs) | Review | Create one-page cheat sheets for all topics |
| Afternoon (2.5 hrs) | Practice | 20 rapid-fire MLOps scenario questions |
| Evening (1.5 hrs) | Plan | Finalize Week 8 schedule |
:::note Week 7 Milestone Checkpoint
Week 8: Final Week -- Confidence and Readiness
Goal: Final refinement, confidence building, and logistics.
Daily time: 3 hours (weekdays), 5 hours (weekends)
Monday -- Light Review
| Time | Activity | Details |
|---|
| Morning (60 min) | Coding | 2 easy/medium problems for flow |
| Lunch (20 min) | Read | Negotiation and Offers |
| Evening (60 min) | Review | Skim all cheat sheets |
| Night (15 min) | Rest | Relax |
Tuesday -- Final Mock
| Time | Activity | Details |
|---|
| Morning (60 min) | Warm-up | 1 easy problem |
| Afternoon (3 hrs) | Mock | Full loop: all rounds simulated |
| Evening (30 min) | Debrief | Final notes on strengths |
Wednesday -- Targeted Review
| Time | Activity | Details |
|---|
| Morning (60 min) | Study | Revisit weakest area |
| Evening (90 min) | Practice | 3 targeted problems |
Thursday -- Behavioral Polish
| Time | Activity | Details |
|---|
| Morning (60 min) | Practice | All STAR stories aloud |
| Evening (90 min) | Mock | Final behavioral mock |
Friday -- Rest
| Time | Activity | Details |
|---|
| Morning (30 min) | Logistics | Confirm schedule, test A/V |
| Afternoon-Evening | Rest | Recharge completely |
Weekend -- Light and Rest
Light review on Saturday. Full rest on Sunday. You are ready.
:::note Week 8 Final Assessment
MLOps Scenario Questions Bank
Practice answering these in under 5 minutes each:
- A model's latency has increased 3x after a routine retraining. What do you investigate?
- A data scientist wants to deploy a model trained in a Jupyter notebook. How do you help them?
- GPU utilization is at 15% across your training cluster. How do you optimize?
- You notice data drift in a production model's input features. What is your response plan?
- The model registry has 200 models with no documentation. How do you implement governance?
- A training pipeline fails at 90% completion on a 48-hour job. How do you make it resilient?
- Two teams need conflicting versions of the same Python library in their training environments. How do you handle this?
- A regulatory audit requires you to reproduce a model that was deployed 6 months ago. Can you?
- The ML platform costs $500K/month. The CFO wants 30% reduction. What do you propose?
- A model needs to serve 100K requests per second with sub-10ms latency. Design the serving layer.
Essential Resources
Handbook Chapters to Prioritize
Books and References
- "Designing Machine Learning Systems" by Chip Huyen
- "Reliable Machine Learning" by Cathy Chen et al.
- "Building Machine Learning Pipelines" by Hannes Hapke
- Google's "MLOps: Continuous delivery and automation pipelines in machine learning"
Next Steps
You now have a complete 8-week roadmap for MLOps Engineer interview preparation. If this path does not match your target role, consider:
The infrastructure that makes ML reliable is just as important as the models themselves. Start building your knowledge today.
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