Top 50 MLOps Interview Questions and Answers

1. What is MLOps and the key components of MLOps?

Machine Learning Operations is the intersection of Machine Learning, DevOps, and Data Engineering which focus on automating and streamlining the end-to-end ML lifecycle, from development to deployment and monitoring.

Key components of MLOps:

Model development
Model versioning
Model validation
CI/CD pipelines
Model deployment
Monitoring & logging
Governance & compliance

2. What are the common tools used in MLOps?

The Common Tools used in MLOps are:

Model training: TensorFlow, PyTorch
Version control: Git, DVC
CI/CD: Jenkins, GitHub Actions
Deployment: Docker, Kubernetes, MLflow
Monitoring: Prometheus, Grafana, Evidently

3. Explain about data versioning and why is it important?

Data versioning focuses on tracking changes to datasets used in the training. It ensures reproducibility and consistency across model versions.

4. Differentiate MLOps from DevOps

DevOps focuses on software delivery automation whereas MLOps handles unique ML challenges such as:

Data versioning
Model training
Model drift management

5. What is model drift and model reproducibility?

Model drift occurs when a model’s performance degrades over time due to changes in data or environment such as data distribution shift and concept drift.

Reproducibility ensures that a model can be retrained and yield the same results using the same code, data, and environment.

6. What is ML pipeline?

ML pipeline automates the workflow from data ingestion, preprocessing, training, evaluation to deployment and monitoring.

7. How do you implement CI/CD for ML models?

CI/CD can be implemented by integrating code, automating testing, validation, deployment and model changes into a CI/CD pipeline using tools like:

Jenkins
GitHub Actions

8. What is A/B testing in ML?

A/B testing is performed to compare two model versions A and B to evaluate which performs better on production data.

9. What is the role of Docker in MLOps?

In MLOps, Docker packages ML models and environments into containers, enabling consistent deployment across platforms.

10. State the phases in the life cycle of MLOps

Life Cycle of MLOps:

Data Collection & Processing
Model Development & Training
Model Validation
Model Deployment
Monitoring & Feedback
Model Retraining

11. How do you monitor a machine learning model in production?

In production, monitoring a machine learning model is crucial to ensure it performs as expected over time. The Key aspects include:

Prediction Accuracy Monitoring
Data Drift Monitoring
Concept Drift Monitoring
Latency and Throughput Monitoring
Resource Utilization
Error logs and Anomalies

12. How is Kubernetes used in MLOps?

Kubernetes orchestrates containerized ML workloads, enabling scalability, resource management, and deployment automation.

13. How can MLOps help with compliance?

MLOps help with compliance by tracking data lineage, model versions, and experiment history that enables auditability and ensures regulatory compliance.

14. What is the purpose of model registry?

Model registry stores, manages, and versions trained ML models, along with metadata, for tracking and deployment.

15. Explain MLflow.

MLflow is an open-source platform for managing the ML lifecycle, including:

Tracking experiments
Packaging code
Deploying models

16. What is the difference between online and batch inference?

Online inference focuses on Real-time predictions for individual inputs while Batch inference are predictions on a batch of data at scheduled intervals.

17. How do you monitor an ML model in production?

In production, monitor and ML model by tracking performance metrics, input data distribution, and detecting anomalies or drift using tools like:

Prometheus
Grafana

18. What is DVC and how is it used?

Data Version Control is used to version datasets and models that enable reproducible and collaborative ML development.

19. What is the role of a model card?

Model card is a documentation that describes various details, such as:

Model’s performance
Use cases
Limitations
Ethical considerations

20. How can you ensure model fairness?

Model fairness is ensured by evaluating model performance across different demographic groups. It is done by applying fairness metrics and bias mitigation techniques.

21. How does explainability help in MLOps?

Explainability helps to understand the model decisions which is crucial for trust, compliance, and debugging.

22. What is shadow deployment and Canary deployment?

Shadow deployment runs a new model alongside the existing one to test performance under real traffic.

Deployment strategy where a new model is gradually rolled out to a small portion of users to minimize risk.

23. What are the challenges in MLOps?

Challenges in MLOps:

Data and model versioning
Managing model drift
CI/CD for ML
Monitoring complex pipelines
Ensuring compliance and reproducibility

24. What is the role of metadata in MLOps?

Metadata captures information about datasets, models, experiments, and pipelines, aiding in traceability and debugging.

25. How do you ensure consistency and collaboration where multiple teams are working on different models?

To ensure consistency and collaboration between multiple team, follow the below steps:

Standardize pipelines using templated CI/CD
Use centralized model registry and feature store
Adopt version control for data and models
Document using model cards

26. A model performs well offline but poorly online. What could be wrong?

Training-serving skew
Inconsistent feature engineering
Data leakage
Poor data quality in production

27. What is hyperparameter tuning and how is it automated?

Hyperparameter tuning optimizes model parameters using techniques like grid search, random search, or Bayesian optimization, often automated using tools like Optuna or Hyperopt.

28. How do you set up an ML pipeline using Kube Flow?

Kube Flow Pipelines is used to define, schedule, and monitor complex ML workflows. To set up an ML pipeline:

Define components using Python SDK or YAML
Connect them in a DAG
Execute using the Kube flow UI

29. How do you deploy a trained ML model using Flask and Docker?

In a Flask API, wrap the model and build a Dockerfile with dependencies. Expose the Flask app via a container and Use docker build and docker run to deploy

30. How do you perform data versioning using DVC?

Initialize with dvc init and Use dvc add data/ to track datasets
Commit .dvc files and push data to remote storage
Use dvc pull or dvc repro to sync

31. How do you automate retraining of a model when new data arrives?

In Airflow or Jenkins, Use scheduled workflows that trigger model retraining pipelines on detecting changes in data storage like S3, Azure Blob, etc.

32. How do you monitor model performance post-deployment?

Collect predictions and ground truths
Calculate metrics like accuracy, precision, recall
Use tools like Prometheus + Grafana or Evidently for alerts

33. How do you handle failed training jobs in your pipeline?

In pipeline orchestrators, Use retries and failure handlers. Implement logging and alerts for debugging. Store failed run metadata for review

34. How do you manage secrets like API keys in your ML pipeline?

To inject keys securely at runtime, Use secret managers like AWS Secrets Manager, Azure Key Vault or Kubernetes secrets.

35. How do you serve a model using TensorFlow Serving?

Export the model as a SavedModel and Use TensorFlow Serving Docker image. Mount the model and expose gRPC/REST endpoints

36. How do you ensure your model meets performance benchmarks before deploying?

To compare metrics (e.g., F1, AUC) against thresholds, use automated evaluation steps in the CI/CD pipeline before approval for deployment.

37. What is continuous training (CT) in MLOps and how do you implement it?

Continuous Training retrains models automatically when new data becomes available using scheduled pipelines and version control.

38. How do you create a model registry using MLflow?

To register a model with a version, Use mlflow.register_model() and manage via MLflow UI or API.

39. Explain the architecture of a real-time ML inference system.

Data ingestion (Kafka, Pub/Sub)
Feature transformation (Feature Store)
Model inference (REST/gRPC)
Monitoring and feedback loop

40. How do you handle multi-model serving?

To handle multi-model serving, use tools like KFServing or Seldon Core that support routing, scaling, and versioning of multiple models via REST or gRPC.

41. How do you version models and experiments effectively?

Version models and experiments by
Combining Git for code,
DVC for data,
MLflow or Weights & Biases for experiments and model versions.

42. What is the role of metadata management in MLOps pipelines?

The role of metadata management in MLOps pipeline enables tracking model lineage, reproducibility, and auditability by logging datasets, hyperparameters, results, and environment info.

43. What are the best practices for data validation in ML pipelines?

The best practices for data validation is to use tools like TensorFlow Data Validation or Great Expectations to validate schema, data drift, missing values, and ranges.

44. How do you secure ML models in production?

Secure ML models in production by authenticating API endpoints and Rate limit requests. Encrypt sensitive data and use model watermarking or explainability to prevent misuse

45. What are the challenges with deploying models on edge devices?

Limited compute/memory
Latency requirements
Model quantization/pruning needed
No real-time cloud access for updates

46. How do you use feature stores in MLOps?

Feature stores like Feast store, retrieve, and serve features for training and inference, ensuring consistency and reusability.

47. What is an ML metadata store and which tools offer it?

An ML metadata store captures pipeline runs, parameters, artifacts and the Tools include are:
MLflow, Kube Flow Metadata, and Vertex AI Metadata Store.

48. What do you do if deploying a model leads to accuracy drop suddenly?

Check for data drift or concept drift and Compare input feature distribution. Analyze logs and predictions and Retrain with updated data if needed

49. State the approaches if your model takes too long to predict in production?

Optimize model using quantization and pruning. Use faster inference engines like ONNX, TensorRT.
Serve via batch prediction if possible and Profile bottlenecks

50. How do you address if a stakeholder asks for model decisions explainability?

Use tools like SHAP, LIME, or Captum and Provide feature attribution charts, including an “Explain” API endpoint

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Top 50 MLOps Interview Questions and Answers