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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GPU vs CPU for ML: When You Actually Need a GPU
GPUs win on large, parallel maths and lose on small, scattered work. Measured on a laptop's integrated GPU and its CPU, the GPU ran at half the CPU's speed on tiny matrices, up to nine times faster on large ones, and nearly four times faster embedding sentences in batches, but slower one sentence at a time.
How to Build an MLOps Portfolio That Stands Out
An MLOps portfolio is judged on evidence that a system works and keeps working, not on how many tools it names. This guide covers what to build, what reviewers look for, how to structure the repository and README, how to use GitHub's profile features, and a rubric for checking your own project.
Model Compression: Quantization and Pruning Explained
Quantization, pruning and distillation all promise smaller models. On a real sentence encoder and 3,080 banking queries, int8 weights cut the file by a third at no accuracy cost, quantizing activations too ran 125 times slower on this CPU, and pruning half the weights without retraining cost seven points.
MLOps Engineer Salary in India 2026: Why ML Plus Infrastructure Pays
MLOps sits where machine learning meets infrastructure, and real 2025 survey data suggests that is exactly where pay rises. ML people who also run containers and Kubernetes were paid well above same-experience peers, and so were infrastructure-skilled developers in India.
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.
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.