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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Docker for Machine Learning: A Practical Guide
How to containerise a machine-learning project so it runs the same everywhere: pinning the exact library versions a saved model depends on, a Dockerfile ordered for fast rebuilds, keeping data out of images, multi-stage builds, non-root users and GPU access.
Machine Learning Roadmap for Beginners in 2026
A machine learning roadmap for beginners in 2026 focused on the models themselves: which maths to learn and in what order, the supervised and unsupervised methods to master, when to start deep learning, how to choose a specialisation, and a concrete self-test at each stage so you know when to move on.
CI/CD for Machine Learning: Pipelines That Work
CI/CD for machine learning has to test three things that change: code, data and models. This guide runs five commits through a staged pipeline on real wine data. It stops a broken delivery, a feature bug and a weaker model at different stages, and shows duplicates had inflated the AUC from 0.822 to 0.896.
Deep Learning Guide: Neural Networks Explained Simply
Neural networks explained by building one: a tiny network written in NumPy learns a problem no straight line can solve, showing layers, activation functions, the loss, backpropagation and gradient descent step by step. Then the same model in a few lines of PyTorch, and when deep learning is and is not the right tool.
How to Read a Machine Learning Research Paper
How to read a machine learning research paper without drowning in it: the three-pass method applied to the Transformer paper, the questions to ask of every results table, the difference between what a paper shows and what it suspects, and a small NumPy re-implementation that checks one of its claims.
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.