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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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Model Context Protocol (MCP) Explained for Beginners
MCP is an open standard for connecting AI applications to tools and data. Instead of writing a custom integration for every assistant, you write one server and any compatible application can use it. This guide explains the parts and runs a real server and client, so you can see exactly what passes between them.
Continuous Training: Automating Model Retraining
Continuous training retrains models automatically, but retraining is only as good as the checks around it. On three years of real household energy data, this guide compares four policies: never retraining, monthly retraining, adding data checks, and adding a champion gate that was right only 21 times in 34.
vLLM and Model Serving: Scaling LLM Inference
Running a model for one user is easy. Serving it to hundreds at once is a scheduling and memory problem. This guide explains decode steps, batching and the key-value cache, simulates why continuous batching keeps a GPU busy, and shows how vLLM is started and called through an OpenAI-compatible API.
Model Packaging: From Pickle to Production
A trained model has to leave the notebook as a file, and the format decides how safe, portable and fast it is. This guide shows why loading a pickle can run arbitrary code, then saves one model as pickle, joblib, skops and ONNX and compares their size, their predictions and what each needs to load.
Batch vs Real-Time Model Inference: Tradeoffs
Nightly batch scoring is cheap and simple; predicting on request is fresh but costlier. This guide measures both on six months of hourly bike rentals, where nightly scoring with midnight's weather had 42% more error than using the current hour's, and sets out when each pattern fits.
Scaling Model Training with Distributed Computing
Distributed training splits the work across machines, but keeping them in step costs time. In real PyTorch data-parallel runs with one, two and four workers, every replica stayed identical, yet small batches trained slower with more workers and only large batches gained, about one and a half times with four.