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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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Edge ML Deployment: Running Models on Devices
Edge deployment runs models on phones and devices instead of servers. With real smartphone sensor data, this guide exports an activity model to ONNX, quantizes it to a 39 KB file at almost no accuracy cost, and shows the bigger risk on devices is new people: accuracy ranged from 81% to 99.5% across volunteers.
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.
Composite Models and DirectQuery in Power BI
DirectQuery and composite models in Power BI, from Microsoft's guidance: why import is the default, the situations that justify DirectQuery, its limits from the one-million-row cap to query folding, how composite models combine import, DirectQuery and dual tables, and the security and performance traps of mixing.
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.