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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
MLOps interviews test whether you can keep a model useful after deployment. This guide maps the rounds with model answers, then works a real case: a fare model trained on January 2024 taxi trips got worse in January 2025, while every drift metric stayed calm and retraining barely helped.
Continuous Training: Automating Model Retraining
Continuous training retrains models automatically, but retraining is only as good as the checks around it. On three years of real household energy data, this guide compares four policies: never retraining, monthly retraining, adding data checks, and adding a champion gate that was right only 21 times in 34.
Logging and Observability for ML Systems
Metrics show that something changed; logs and traces show which requests, which step and which model version. This guide logs every prediction of a real intent classifier as JSON, traces each request with OpenTelemetry and joins the true labels later, finding that 9% of requests held 41% of the errors.
What Is MLOps? DevOps for Machine Learning Explained
MLOps is the practice of building machine learning systems that keep working after launch. This guide defines it, explains why ML needs more than DevOps, and shows the problem it solves on real data: a model built in 2011 that was 38% wrong through 2012, and how much the way you retrain it matters.
Feature Stores Explained: Why Teams Need Them
A feature store keeps the features a model trains on and the features it is served the same. This guide builds one with Feast on a year of real retail orders and shows the problem it solves: a naive join that looked better in validation, 0.798 against 0.727, then did worse in production.
Scaling Model Training with Distributed Computing
Distributed training splits the work across machines, but keeping them in step costs time. In real PyTorch data-parallel runs with one, two and four workers, every replica stayed identical, yet small batches trained slower with more workers and only large batches gained, about one and a half times with four.