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.
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Model Versioning with MLflow and DVC
A model version is only useful if you can say what produced it. This guide combines DVC for data with MLflow for models, so each registered version records its data hash and Git commit, then rebuilds an old version from that record alone and checks that the predictions come out identical.
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.
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.
Model Explainability in Production with SHAP
SHAP turns a prediction into per-feature contributions you can show a customer or an auditor. On 30,000 real credit card accounts, this guide produces reason codes for declined applicants, checks that explanations add up exactly, and shows an equally valid explainer setting changing the reasons for 42% of them.
Model Deployment: From Notebook to Production
The steps between a model that works in a notebook and one people can rely on: choosing batch or online serving, turning the notebook into a reproducible pipeline, a concrete case of training-serving skew and how a saved pipeline prevents it, a small validated prediction API, and what to monitor after launch.
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.