Welcome to Part 4 of our Machine Learning Interview Questions Series. In this post, we explore questions centered on deploying, maintaining, and scaling ML systems in production environments. These are the operational topics every ML engineer or data scientist should understand to bridge the gap between experimentation and real-world impact. Whether you’re prepping for interviews at product-based companies or contributing to production ML workflows, mastering these topics ensures you’re seen as more than just a model builder—you’re a full-stack ML engineer.
31. What are the common ways to deploy a machine learning model?
Deployment methods vary depending on use cases:
- Batch Inference
- Predictions run at scheduled intervals
- Ideal for reporting, scoring large datasets
- Online Inference (Real-time APIs)
- Serve predictions via HTTP endpoints
- Used in applications like fraud detection, recommendations
- Edge Deployment
- Models run on-device (e.g., mobile, IoT)
- Useful for low-latency or offline use cases
- Streaming Inference
- Models consume real-time data streams
- Tools: Apache Kafka, Apache Flink
Key Tools: Flask, FastAPI, Docker, Kubernetes, TensorFlow Serving, MLflow, TorchServe
32. What are the main components of a production ML system?
A production-ready ML system typically includes:
- Data ingestion pipeline (e.g., Airflow, Spark)
- Feature store (e.g., Feast)
- Model versioning & registry (e.g., MLflow, DVC)
- Model serving infrastructure
- Monitoring tools for performance & drift detection
- CI/CD pipelines for automated testing & deployment
These components ensure that ML models are scalable, reproducible, and maintainable in real-world environments.
33. What is model monitoring and why is it important?
Model monitoring tracks how an ML model performs after deployment to ensure continued reliability.
Monitored Metrics:
- Prediction accuracy or error
- Data drift (input distribution change)
- Concept drift (label distribution change)
- Latency and uptime
Tools:
- Prometheus + Grafana
- Evidently AI
- WhyLabs
- Arize AI
Importance: Without monitoring, silent model failures can result in business losses or degraded user experience.
34. What is CI/CD in Machine Learning?
CI/CD (Continuous Integration / Continuous Deployment) ensures consistent and automated model delivery.
- CI: Tests data pipelines, model performance, and code changes automatically
- CD: Automates deployment of models to staging or production
Tools:
- GitHub Actions, Jenkins, GitLab CI
- Kubeflow Pipelines, Vertex AI Pipelines
- MLflow + Docker + Kubernetes
Benefits: Speeds up iteration, improves reliability, and minimizes human error in deployment.
35. How do you handle versioning in ML?
Versioning in ML involves tracking:
- Code (Git)
- Data (DVC, Delta Lake)
- Models (MLflow, Weights & Biases)
- Pipelines (Kubeflow, Airflow DAGs)
This ensures reproducibility, rollback capability and collaboration across teams. A proper versioning strategy is critical in regulated or high-risk domains.
36. How do you optimize the cost of ML inference?
Optimizing inference cost is key for scalable ML systems.
Techniques:
- Model quantization (e.g., 8-bit precision)
- Model pruning (removes redundant weights)
- Serverless inference (on-demand scaling)
- Batching requests
- Choosing the right hardware (e.g., CPU vs GPU vs TPU)
- Auto-scaling with Kubernetes
Cost-efficiency is not just a DevOps task—ML engineers must design models with operational constraints in mind.
37. What are the differences between monolithic and microservice ML deployment?
| Aspect | Monolithic | Microservices |
|---|---|---|
| Structure | Single large app | Small, modular components |
| Scalability | Hard to scale independently | Easy to scale individual parts |
| Flexibility | Tightly coupled | Loosely coupled (e.g., feature service, model API) |
| Use case | Prototypes, MVPs | Production-grade systems |
Microservices allow for better version control, testing, and horizontal scaling of components.
38. What is model reproducibility?
Reproducibility means you can consistently re-create the model’s output using the same data, code, and configuration.
Requires:
- Fixed random seeds
- Logged data snapshots
- Environment tracking (e.g., Python, dependencies)
- Version control for code + model + data
Important for regulatory compliance, debugging, and collaboration across teams.
39. What are some open-source ML deployment and orchestration tools?
Popular Tools:
- MLflow: Model tracking, registry, and serving
- Airflow / Prefect: Orchestrate data & training pipelines
- KubeFlow: End-to-end ML pipelines on Kubernetes
- Seldon Core / BentoML / Triton Inference Server: Scalable model serving
- Feast: Feature store for online & offline use
These tools help manage the ML lifecycle beyond just training.
40. What are some challenges in deploying ML models?
Common challenges include:
- Data pipeline breakage
- Feature skew between training and serving
- Model drift and decay
- Scaling inference for real-time applications
- Security and access control
- Cross-team coordination (ML + DevOps)
Overcoming these requires a well-architected ML system, robust testing and close collaboration between data science and engineering.
Conclusion
In Part 4 of the Machine Learning Interview Questions Series, we explored the production side of machine learning from deployment strategies to monitoring, versioning, and cost optimization. These operational skills are what differentiate research ML from real-world ML.
Mastering these questions prepares you not only for technical interviews but also for building systems that work reliably in production environments.
Stay tuned for Part 5, where we’ll explore compliance in ML systems, data privacy, fairness, and responsible AI — increasingly important topics in today’s AI-driven world.
Related Read
Machine Learning Interview Questions – Part 3