MLOps and Model Deployment Interview Questions for Data Science

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MLOps is the set of practices that take machine learning models from experiments to reliable production and keep them working. It covers versioning data and code, tracking experiments, automated training pipelines, testing, deployment, monitoring and retraining. It borrows ideas from DevOps but adds data and model concerns, because a model can fail even when the code has not changed, simply because the data has.

What is the difference between batch and real-time inference?

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Batch inference scores many records on a schedule, for example nightly churn scores for all customers written to a table. It is cheap and simple. Real-time inference serves predictions on request through an API within milliseconds, for example fraud checks during payment. It needs low-latency feature access, scaling and high availability. Choose real-time only when the decision truly must use fresh data at the moment of the request.

What is an Airflow DAG?

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A DAG, directed acyclic graph, in Apache Airflow defines a workflow as tasks with dependencies, such as extract data, build features, train model and publish scores, in that order. Airflow schedules runs, retries failed tasks, passes run dates and shows status in a web UI. Acyclic means no task can loop back to an earlier one. Data scientists use it for scheduled training and batch scoring pipelines.

What is a model registry and why do teams use one?

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A model registry is a central store for trained model versions along with their metadata: training data version, code commit, parameters, metrics and owner. Models move through stages such as staging, production and archived, with approvals. It gives one source of truth for which model is live, makes rollback to a previous version easy and supports audits. Tools like MLflow Model Registry are commonly used.

How do you detect and treat concept drift?

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Monitor input distributions and live metrics, compare to training baselines, and retrain or recalibrate on fresh data when performance drops.

What is experiment tracking and why use a tool like MLflow?

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Experiment tracking records each training run's parameters, code version, data version, metrics and output artefacts. Tools like MLflow or Weights and Biases store this in one place, so you can compare runs, reproduce the best model and see why something improved. Without it, results live in notebook outputs and memory. MLflow also offers a model registry to move approved models through staging and production.

Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.

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from fastapi import FastAPI from pydantic import BaseModel import joblib import pandas as pd app = FastAPI() model = joblib.load('model.joblib') class Customer(BaseModel): tenure: int monthly_spend: float plan: str @app.post('/predict') def predict(c: Customer): X = pd.DataFrame([c.model_dump()]) return {'churn_probability': float(model.predict_proba(X)[0, 1])}

Write a Dockerfile for a Python model API.

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FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app/ ./app COPY model.joblib . EXPOSE 8000 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

What does CI/CD mean for machine learning projects?

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Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.

What is the difference between saving a model with pickle, joblib and ONNX?

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pickle serialises Python objects; it is simple but tied to Python and library versions, and loading untrusted pickle files is a security risk. joblib is similar but more efficient for objects with large NumPy arrays, like scikit-learn models. ONNX is an open, language-neutral format that runs in optimised runtimes across languages and hardware, useful for fast serving. Always store library versions alongside pickle or joblib files.

Your model performance degrades every few months. Would you retrain on a fixed schedule or based on triggers?

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Scheduled retraining, like monthly, is simple and predictable but may be too late after a sudden change or wasteful when nothing changed. Trigger-based retraining runs when monitoring shows drift or a performance drop, which reacts faster but needs reliable monitoring. Many teams combine both: a regular schedule plus triggers for big shifts. Either way, a retrained model must pass the same evaluation gate before replacing the current one.

How do you make a machine learning experiment reproducible?

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Version the code with Git, the data with a tool like DVC or dated snapshots, and the environment with pinned requirements or Docker. Set random seeds for every library. Log parameters, metrics and the exact data version for each run with a tracker like MLflow. Keep preprocessing inside a pipeline rather than manual notebook steps. Then anyone should be able to rebuild the same model from a commit and a data version.

What should you log for a production prediction service?

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For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.

Your deployed model gives different predictions than the same model in your notebook. Why?

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Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.

Write pytest unit tests for a feature function that computes days since last purchase.

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import pandas as pd from features import days_since_last_purchase def test_basic_case(): df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.loc[1] == 5 def test_no_future_orders_used(): df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.empty

Tell me about a time you deployed a model under a tight deadline.

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Situation: a pricing model had to go live before a sale in ten days. Task: deploy safely in that window. Action: I cut scope to batch scoring instead of real-time, reused our standard Docker and Airflow template, wrote tests for the key features and set up a dashboard for score distribution. Result: it went live two days early and ran without incidents during the sale. Real-time scoring followed a month later.

Tell me about a time you disagreed with a stakeholder about which metric to optimise.

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Situation: marketing wanted the lead model judged on overall accuracy. Task: agree on a metric that matched the goal. Action: I showed that accuracy rewarded predicting no conversion for everyone, then proposed precision in the top 1,000 leads per week, which matched the sales team's call capacity, with a simple chart. Result: they agreed, and the model with the better top-1,000 precision added 18 percent more conversions.

Soon after a new model version went live, customer complaints spiked. How do you roll back safely and learn from it?

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Roll back immediately to the previous registry version through the deployment pipeline, rather than editing servers by hand, and confirm metrics recover. Then investigate offline: compare predictions of both versions on the same traffic, check feature pipelines and data changes, and review what the offline evaluation missed. Add the failing cases to the test set, and use canary or shadow rollout with alert thresholds for future releases.

Write Python code that validates prediction requests with Pydantic before passing them to a model.

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from pydantic import BaseModel, Field, ValidationError class LoanRequest(BaseModel): age: int = Field(ge=18, le=100) income: float = Field(gt=0) city: str = Field(min_length=2) def predict(payload: dict): try: req = LoanRequest(**payload) except ValidationError as e: return {'error': e.errors()} return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}

Tell me about a time you automated a manual step in a machine learning workflow.

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Situation: each monthly retrain needed a day of manual data pulls, notebook runs and copying files to servers. Task: make it reliable and hands-off. Action: I wrapped the steps into a scheduled pipeline with data validation checks, logged runs in MLflow and added an approval step before promotion. Result: retraining time dropped from a day to under an hour of review, and there were no more failed deployments from missed steps.

How do you evaluate and explain a model to business stakeholders?

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Tie metrics to business outcomes such as cost saved, show feature importance or SHAP values, give example predictions and be transparent about limitations.

How would you build a churn prediction model end to end?

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Define churn and the prediction window, build behavioural features, split by time, train and compare models, evaluate with recall and lift, explain drivers and monitor drift after deployment.

How do you deploy and monitor a machine learning model?

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Package the model behind an API with versioning, track latency and errors, monitor input drift and prediction quality, and automate retraining with rollback.

Design an end-to-end churn prediction system.

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Define churn and the prediction window → build usage and billing features → handle imbalance → train gradient boosting → evaluate with PR-AUC and lift → explain with SHAP → deploy as batch scoring or an API → monitor drift → hand top-risk users to the retention team.

Write Python code to compute the Population Stability Index for a feature to detect drift.

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import numpy as np def psi(expected, actual, bins=10): edges = np.quantile(expected, np.linspace(0, 1, bins + 1)) edges[0], edges[-1] = -np.inf, np.inf e = np.histogram(expected, edges)[0] / len(expected) a = np.histogram(actual, edges)[0] / len(actual) e, a = np.clip(e, 1e-6, None), np.clip(a, 1e-6, None) return float(np.sum((a - e) * np.log(a / e))) # rule of thumb: <0.1 stable, 0.1-0.25 watch, >0.25 investigate

What is a feature store and what problems does it solve?

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A feature store is a central system to define, compute, store and serve features for models. It keeps an offline store for training with point-in-time correct history and an online store for low-latency serving, both fed by the same feature definitions. This prevents training-serving skew, avoids teams rebuilding the same features, and adds documentation and monitoring. It is worth it when many models share features or need real-time serving.

What are shadow, canary and A/B deployments for models?

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In shadow deployment the new model receives real traffic and logs predictions, but users still get the old model's output; it is safe for checking latency and agreement. A canary sends a small share of traffic, like 5 percent, to the new model and expands if metrics stay healthy. An A/B test splits traffic randomly to measure business impact statistically. Teams often go shadow, then canary, then full rollout.

Your model API meets average latency but p99 latency is 900 ms against a 200 ms target. What do you do?

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Profile slow requests to see if time goes to feature fetching, preprocessing, the model or the network. Common fixes: precompute and cache features, remove slow database calls from the request path, load the model once at start-up, use a faster runtime like ONNX, and warm up instances. Check garbage collection pauses, cold starts and autoscaling limits. Set timeouts with a safe fallback prediction for rare slow cases.

Your fraud model's true labels arrive 30 days late. How do you monitor it in production?

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Since accuracy cannot be measured quickly, monitor proxies. Track input data drift with PSI per feature, prediction score distribution, the share of transactions flagged, and data quality like missing values. Watch early signals such as chargeback complaints or manual review outcomes on a sample. Set alerts on sudden shifts. When labels arrive, compute real precision and recall by cohort and compare them with the proxy alerts to tune them.

How would you design an ML platform for a team of 20 data scientists?

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I would start from the team's pain points, then provide shared building blocks: a standard project template, managed notebooks with access to the warehouse, experiment tracking and a model registry, an orchestrator like Airflow for pipelines, a feature store if real-time use cases exist, a standard serving path using containers, and default monitoring. Prefer managed services over custom builds, and add self-serve documentation so teams do not depend on one platform engineer.

Describe a production incident with a model and how you handled it.

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Situation: one morning our recommendation model started showing the same five products to all users. Task: restore service and find the cause. Action: I rolled back to the previous model version within 20 minutes, then traced the issue to an upstream table that had arrived empty, so all user features were defaults. Result: we added data volume checks before scoring and a blameless postmortem. Rollback readiness saved us that day.

How does Kubernetes help in serving machine learning models?

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Kubernetes runs containerised model services across a cluster. It restarts failed containers, scales the number of replicas based on traffic or CPU, rolls out new versions gradually and can roll back, and schedules GPU workloads on the right nodes. Tools built on it, like KServe or Seldon, add model-specific features such as canary releases and autoscaling to zero. The trade-off is operational complexity, so small teams often use managed services.

Your GPU model-serving bill has doubled while traffic grew only 20 percent. How do you bring cost down?