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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MLOps Roadmap 2026: Skills, Tools and Career Path
MLOps sits between data science, software engineering and operations, which makes it hard to know where to start. This roadmap orders the skills from foundations to production operations, gives each stage a project that proves the skill, and explains what the MLOps engineer role actually involves.
MLflow Tutorial: Tracking Experiments and Models
MLflow records what produced each model so you never have to remember. This tutorial trains three models on real bike-sharing data, logs every run, compares them by querying the tracking store, registers the best one, points a champion alias at it and loads it back by that alias, all on a laptop.
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.
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.
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.
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.