🚀 Ultimate MLOps System Design & Interview Cheat Code

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🔧 MLOps System Design Pipeline (12 Stages)

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1️⃣ Problem Framing & Data Requirements
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2️⃣ Data Collection & Ingestion (APIs, databases, streaming)
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3️⃣ Data Validation & Preprocessing (Schema checks, nulls, scaling)
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4️⃣ Exploratory Data Analysis (EDA)
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5️⃣ Feature Engineering & Transformation
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6️⃣ Model Development & Experiment Tracking (MLflow, W&B)
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7️⃣ Model Evaluation & Validation (cross-validation, metrics)
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8️⃣ Model Versioning & Registry (MLflow, DVC, S3, Git)
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9️⃣ Containerization (Docker, Podman)
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🔟 CI/CD Pipeline Setup (GitHub Actions, Jenkins, GitLab CI)
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1️⃣1️⃣ Deployment (Batch | Real-Time API | Streaming via Flask, FastAPI, KServe)
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1️⃣2️⃣ Monitoring & Feedback Loop (drift, logs, retraining triggers)

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🧠 MLOps Intern Interview Questions & Refined Answers

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📘 Core MLOps Concepts

Q1. What is MLOps?
A: MLOps (Machine Learning Operations) is the set of practices that combine machine learning, DevOps, and data engineering to streamline and automate the end-to-end ML lifecycle—from data ingestion to model deployment and monitoring—ensuring reliability, reproducibility, and scalability in production environments.

Q2. How is MLOps different from DevOps?
A:

Q3. Why is MLOps important in real-world ML systems?
A: It enables faster experimentation, reliable deployments, version control, reproducibility, drift detection, and continuous delivery of machine learning models, thus closing the gap between research and production.

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📊 Data & Model Lifecycle Management

Q4. How do you version data and models?
A:

• Data: DVC, LakeFS, Delta Lake, or custom versioning using hashes & S3 buckets.

• Models: MLflow Model Registry, Git LFS, or custom APIs with tags and metadata.

Q5. How do you track model experiments?
A: Using tools like MLflow, Weights & Biases (W&B), or CometML to log hyperparameters, metrics, code versions, artifacts, and outputs across experiments.

Q6. What’s the difference between model training and serving?
A:

• Training: Building models using historical data.

• Serving: Exposing trained models via REST APIs or batch jobs for real-time inference.

Q7. What are types of data and concept drift?
A:

• Data Drift: Distribution of input data changes.

• Concept Drift: Target variable distribution or underlying relationship changes over time.

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🔧 Tooling & Infrastructure

Q8. What is the role of Docker in MLOps?
A: Docker ensures consistent environments by packaging the application, dependencies, and OS into a container, eliminating "works on my machine" issues across dev and prod.

Q9. What is MLflow, and why is it used?
A: MLflow is an open-source tool used for:

• Tracking experiments

• Packaging ML code into reproducible formats

• Managing model lifecycle via the registry

• Deploying models via REST or local servers

Q10. Difference between MLflow and DVC?
A:

Q11. Why use Kubernetes in MLOps?
A: Kubernetes orchestrates containerized ML workloads by offering scalability, high availability, automated rollouts/rollbacks, and fault tolerance—essential for large-scale ML systems.

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🚀 CI/CD for Machine Learning

Q12. What is CI/CD in MLOps?
A: CI/CD automates the entire ML workflow from code commit to model retraining, testing, packaging, and deployment—reducing manual effort and errors.

CI/CD Workflow Includes:

• Unit testing ML code

• Auto training on new data

• Model performance validation

• Dockerizing & pushing image

• Deployment via API/K8s

Q13. Tools used for MLOps CI/CD?
A: GitHub Actions, GitLab CI, Jenkins, Argo Workflows, CircleCI, and Tekton.

Q14. How does Git help in MLOps?
A: Git handles version control for code, pipeline definitions, notebooks, and model configs. Combined with DVC, it also versions datasets and model files.

Q15. What is Infrastructure as Code (IaC) in MLOps?
A: Tools like Terraform and Ansible define cloud and on-prem infrastructure using code (YAML/HCL) to automate reproducible environments across stages.

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☁️ Deployment, Inference, and Monitoring

Q16. Deployment strategies for ML models?
A:

• Batch Inference: Periodic processing on large datasets.

• Online Inference: Real-time prediction via REST APIs (Flask/FastAPI).

• Streaming Inference: Event-driven, often using Kafka + Spark + ML.

Q17. REST API vs Streaming Inference?
A:

• REST API: Handles single or small requests in real-time.

• Streaming: Handles real-time data flow continuously (Kafka/Redis).

Q18. Monitoring in MLOps?
A: Use Prometheus, Grafana, Seldon Core, and tools like Evidently AI to monitor:

• Prediction accuracy

• Latency

• Data drift

• System resource utilization

Q19. How do you handle drift or model decay?
A:

• Monitor drift metrics (KL divergence, PSI)

• Trigger auto-retraining pipelines

• Use active learning and feedback loops

Q20. How do you secure ML APIs in production?
A:

• Use HTTPS

• Token-based authentication (JWT/OAuth)

• Rate limiting

• Input validation & schema checks

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🧠 Scenario-Based Questions

Q21. Your model performs well in test, but fails in production—what do you do?
A:

• Validate input data schema

• Check feature pipelines (data leakage, missing transformations)

• Look for drift in live input data

• Validate production metrics & logs

Q22. What is your rollback strategy for a failed model deployment?
A:

• Use model versioning from MLflow

• Revert to previous stable version

• Automate rollback via CI/CD with metrics-based triggers

Q23. What does a production-grade ML system look like?
A:

• Modular pipeline

• CI/CD integration

• Scalable serving (via Docker/K8s)

• Monitoring + Alerting

• Retraining loop