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.
Featured Guides
Data Science Roadmap 2026: A Complete Step-by-Step Guide
A data science roadmap in dependency order, from Python and SQL through cleaning, statistics and honest model evaluation to communicating results, with an exit test at every stage and one dataset that carries through the whole path.
What Is Generative AI? A Complete Beginner's Guide
What generative AI is, in plain language: how language models produce text one token at a time (shown with a tiny runnable model), how chat models are trained, how image models differ, what the technology is reliable for, and why it confabulates.
Learn Python from Scratch: A Complete 2026 Roadmap
How to learn Python from zero: install a current version (including the new Windows install manager), then work through seven stages, from values and loops to files, virtual environments, classes and tests, each ending with a small program you should be able to write on your own.
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Python Iterators vs Generators vs Comprehensions
The same task written as an iterator class, a generator function, a generator expression and a list comprehension, then measured for time and peak memory. The numbers show why lists cost memory, why generators do not, when a class is worth its extra code, and how to choose between the four.
Python Lists vs Tuples vs Sets vs Dictionaries
Python's four core collections compared on the properties that decide which to use: order, duplicates, mutability, whether they can be dictionary keys, how fast membership tests are and how much memory they take, with runnable demonstrations and a short set of questions that picks the right one.
Pipeline Dependencies and Scheduling in ADF
Scheduling in Azure Data Factory works at three levels: dependencies between activities, pipelines calling pipelines, and triggers. This guide covers the four dependency conditions, Execute Pipeline, the three trigger types and how they differ, schedule recurrence and time zones, and patterns for ordering work.
Airflow for ML Pipelines: A Practical Guide
Airflow can schedule more than data loads: it can retrain a model, score each day, check the error and raise an alert. This guide builds a training DAG and a scoring DAG on real bike rental data, and shows them handling a normal day, a bad day, and the day Hurricane Sandy cut the data off.
Building an End-to-End MLOps Pipeline
An end-to-end MLOps pipeline joins data validation, versioning, tracking, a registry, serving, monitoring and retraining into one loop. This guide runs that loop on real motorway traffic data: monitoring caught a summer surge, one retrained challenger was rejected, a second was promoted, and 2018 error fell 13%.
Cost Optimization for ML Workloads in the Cloud
Most of an ML cloud bill comes from three decisions: how capacity is bought, how fully it is used and how efficiently each prediction is made. With live Azure prices and two real models, this guide shows reservations and spot cutting prices by 38 to 82%, and an always-on VM costing seven times its compute.