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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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.
Serving ML Models with FastAPI and Docker
Putting a model behind an API is where many projects stall. This guide builds a FastAPI prediction service that loads a model once at start-up, enforces an input contract, serves single and batch predictions, reports health and readiness separately, and shows the Dockerfile that packages it.
Docker and Kubernetes Interview Questions for ML
Container questions in ML interviews are about the mistakes that make images slow to build, huge, insecure or killed in production. Four are answered with real Docker builds and runs, from a cache that survives a code change to a container killed with exit code 137, then the Kubernetes questions that follow.