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.
All articles
Kubernetes for ML: Deploying Models at Scale
Kubernetes runs copies of your model service, replaces the ones that die and swaps versions without downtime, if the manifest is right. This guide deploys a concrete-strength model to a local cluster, kills a pod, rolls out a new version under continuous traffic with no failed requests, and rolls back.
Monitoring ML Models in Production: A Complete Guide
A model that passed validation can fail quietly in production. This guide sets out what to monitor at each layer, how to cope with labels that arrive late and how to design alerts people act on, using a year of real sensor data in which the loudest alert was the least useful one.
How Large Language Models (LLMs) Work Under the Hood
A large language model does one thing: given some text, it scores every possible next token. Everything else, from chat to code, is that step repeated. This guide follows the step with real numbers from GPT-2 running on a laptop, then explains training, temperature and why fluent text is not always true.
Generative AI for Images: Diffusion Models Explained
Diffusion models create images by learning to undo noise. This guide explains the forward noising process, what the network is trained to predict, why sampling takes many steps, and how latent diffusion and text guidance produce today's image generators, with a small NumPy demonstration of the maths.
Multimodal AI: Models That See, Hear and Speak
Multimodal models understand more than one kind of input: text with images, audio or video. This guide shows the core idea, a shared space where a photo and a sentence can be compared, in a small CLIP demo, then explains how today's vision-language assistants are built, what they cost and where they fail.
A/B Testing ML Models in Production
A model that wins offline still has to prove itself with real customers. On real bank marketing outcomes, this guide shows why a one-point offline gain needed about 36,000 calls per arm to confirm, how to split traffic and check the split, and how checking results early turned 4% false alarms into 18%.