In this article, you will learn how to use scikit-LLM’s text summarization feature to handle large volumes of text in machine learning pipelines.
Building AI Agents with Local Small Language Models
In this article, you will learn how to build a fully functional AI agent that runs entirely on your own machine using small language models, with no internet connection and no API costs required. Topics we will cover include: What AI agents and small language models are, and why running them locally is a practical […]
Train, Serve, and Deploy a Scikit-learn Model with FastAPI
In this article, you will learn how to train a Scikit-learn classification model, serve it with FastAPI, and deploy it to FastAPI Cloud.
AI Agent Memory Explained in 3 Levels of Difficulty
In this article, you will learn how AI agent memory works across working memory, external memory, and scalable memory architectures for building agents that improve over time.
Getting Started with Zero-Shot Text Classification
In this article, you will learn how zero-shot text classification works and how to apply it using a pretrained transformer model.
The Complete Guide to Inference Caching in LLMs
In this article, you will learn how inference caching works in large language models and how to use it to reduce cost and latency in production systems.
Python Decorators for Production Machine Learning Engineering
In this article, you will learn how to use Python decorators to improve the reliability, observability, and efficiency of machine learning systems in production.
5 Techniques for Efficient Long-Context RAG
In this article, you will learn how to build efficient long-context retrieval-augmented generation (RAG) systems using modern techniques that address attention limitations and cost challenges.
How to Implement Tool Calling with Gemma 4 and Python
In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama.
Structured Outputs vs. Function Calling: Which Should Your Agent Use?
In this article, you will learn the architectural differences between structured outputs and function calling in modern language model systems.