Machine Learning Mastery Articles · DeepTrendLab

Agentic RAG Explained in 3 Levels of Difficulty

Traditional

Effective KV Compression with TurboQuant

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines —…

Building AI Agents in Python with Pydantic AI

Effective Context Engineering for AI Agents: A Developer’s Guide

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Text Summarization with Scikit-LLM

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Building AI Agents with Local Small Language Models

The idea of building your own AI agent used to feel like something only big tech companies could pull off.

Train, Serve, and Deploy a Scikit-learn Model with FastAPI

FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.

AI Agent Memory Explained in 3 Levels of Difficulty

A stateless AI agent has no memory of previous calls.

Getting Started with Zero-Shot Text Classification

Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.

The Complete Guide to Inference Caching in LLMs

Calling a large language model API at scale is expensive and slow.

Python Decorators for Production Machine Learning Engineering

You've probably written a decorator or two in your Python career.

5 Techniques for Efficient Long-Context RAG

How to Implement Tool Calling with Gemma 4 and Python

The open-weights model ecosystem shifted recently with the release of the

Structured Outputs vs. Function Calling: Which Should Your Agent Use?

Language models (LMs), at their core, are text-in and text-out systems.

Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System