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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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MLOps Interview Questions and Answers, Part 2: The ML System Design Round
In the ML system design round, interviewers want numbers, not boxes. This guide works one design question end to end: a cancellation-risk service for hotel bookings. It checks the label and leakage, compares three models on quality, size and latency, and sizes the serving fleet from measurements rather than guesses.
Pipeline Orchestration: Airflow vs Prefect vs Dagster
Machine learning pipelines ask more of an orchestrator than nightly ETL: skipping expensive steps that have not changed, training many segment models with retries, and knowing which model came from which data. This guide runs those needs in Prefect and Dagster on real data, and maps them to Airflow.
Prometheus and Grafana for ML Monitoring
Prometheus and Grafana are the usual open-source pair for watching services. This guide instruments a real model API, scrapes it with Prometheus 3.15, unit-tests a drift alert, provisions a Grafana 13 dashboard from files and replays two real weeks of requests to see what the setup catches and what it misses.
Real-Time Feature Engineering for Online Models
Online models need features from events seconds old, and those features must match the ones the model was trained on. On a month of real New York taxi pickups, this guide builds a streaming window counter, shows a one-character boundary change breaking 3.7% of lookups, and measures what a two-minute lag costs.
Secrets Management for ML Pipelines
ML pipelines handle more credentials than most code, and leak them in places ordinary software does not, such as experiment trackers. This guide plants AWS's published example key in a project and follows it: a scanner flags it, MLflow stores it in plain text, a log filter redacts it and a vault keeps it out.
Shadow Deployment and Canary Releases for ML
Shadow mode runs a new model on real traffic without letting it answer; a canary lets it answer for a few users first. On real e-commerce sessions, this guide shows a unit bug that 89% agreement hid, the flag-rate guardrail that caught it, and why per-stage business metrics were too noisy to gate on.