IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
All articles
A/B Testing ML Models in Production
A model that wins offline still has to prove itself with real customers. On real bank marketing outcomes, this guide shows why a one-point offline gain needed about 36,000 calls per arm to confirm, how to split traffic and check the split, and how checking results early turned 4% false alarms into 18%.
Blue-Green Deployments for ML Services
Blue-green deployment keeps the old model serving while the new one waits beside it, so switching either way is quick. This guide runs it with two real spam filters, shows a pre-switch check catching a score-scale change that would have made a review queue five times longer, and switches under live traffic.
GitHub Actions for ML: Automating Workflows
GitHub Actions can test, train and gate a model on every change. This guide writes a complete workflow for a real concrete-strength model, runs its test and training steps locally, and lints it with actionlint, which caught four of five planted mistakes and missed one that only a careful review would find.
Model Drift and Data Drift: Detection and Response
Most drift tooling watches a model's inputs. This guide shows, on three Januaries of real New York taxi trips, a change that left every input statistic flat while the model's error grew fivefold, why significance tests mislead at scale, and how to respond to drift you detect and to change you know is coming.
Edge ML Deployment: Running Models on Devices
Edge deployment runs models on phones and devices instead of servers. With real smartphone sensor data, this guide exports an activity model to ONNX, quantizes it to a 39 KB file at almost no accuracy cost, and shows the bigger risk on devices is new people: accuracy ranged from 81% to 99.5% across volunteers.
Containerizing a Machine Learning Model Step by Step
Most container tutorials stop when the container starts. This walkthrough containerizes a real scikit-learn model step by step, then measures what matters: build and rebuild times, image size, the user it runs as, time to healthy, memory, and whether the container's predictions match training exactly.