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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BigQuery ML: Machine Learning with SQL
BigQuery ML trains and runs machine learning models with SQL, on data that never leaves the warehouse. This guide covers the statements, model types and pricing, then reproduces a logistic regression locally on 300,000 real trips to show what the evaluation numbers mean and three ways an easy model can mislead.
Testing Machine Learning Code and Data
ML code can pass every unit test and still ship a broken model, because data and model behaviour need tests of their own. This guide writes nine pytest checks for a real model, covering schema, invariants, plausibility, a baseline gate and behaviour, and one catches a genuine sensor fault in the data.
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.
Cross-Validation in Machine Learning: A Practical Guide
Cross-validation done properly: what cross_val_score does by default, how to pick the splitter your data actually needs (stratified, grouped or time-ordered), and a runnable demonstration of how preprocessing outside a pipeline turns random noise into an 85% score.
End-to-End Machine Learning Project: A Complete Walkthrough
A complete machine learning project walked through on real housing data: framing the question and the metric, locking away a test set, beating a baseline, comparing models with cross-validation, a single honest test score, finding where the model fails, and saving it for use, with every number from real runs.
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.