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 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.
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.
Deploying ML Models on AWS, Azure and GCP
AWS, Azure and Google Cloud all host models behind managed endpoints, but each expects a different serving contract. This guide runs one real model behind all three contracts locally, including Microsoft's own inference server, and shows what changes between clouds and what should stay exactly the same.
From Data Scientist to MLOps Engineer: A Roadmap
Data scientists already know models, data and evaluation, which is most of what MLOps engineers wish their colleagues knew. The gap is software and operations. This plan sets out what transfers, what to learn, a twelve-week sequence built around one evolving project, and how to present the move to employers.