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Versioning and Tracking Scikit-LLM Experiments
4+ day, 18+ hour ago (235+ words) Making developers awesome at machine learning In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow. Topics we will cover include: Registering, versioning, and comparing scikit-learn-like…...
Dataclasses for Structured Application Data
1+ week, 2+ day ago (448+ words) Making developers awesome at machine learning In this article, you will learn how Python’s dataclass decorator can replace fragile configuration dictionaries with structured, readable, and maintainable data models. Topics we will cover include: Here’s the loose dictionary’s replacement in its…...
Single-Agent vs. Multi-Agent Systems: When the Complexity Is Worth It
1+ week, 3+ day ago (1109+ words) Making developers awesome at machine learning In this article, you will learn the key differences between single-agent and multi-agent AI systems, and how to decide which architecture fits your problem. Topics we will cover include: Everyone building with AI hits…...
AI Agent Memory Design: What Works and What Doesn’t
1+ week, 4+ day ago (1347+ words) Making developers awesome at machine learning In this article, you will learn how to design reliable memory systems for AI agents, covering both the patterns that work and the common architectural mistakes that cause persistent, hard-to-trace failures. Topics we will…...
Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline
1+ week, 6+ day ago (287+ words) Making developers awesome at machine learning In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with structured tabular features for classification tasks. Topics we will…...
Interpretable Text Classification: Probing Scikit-LLM Embedding Spaces
2+ week, 2+ day ago (464+ words) Making developers awesome at machine learning In this article, you will learn how to use probing classifiers, UMAP visualization, and SHAP values to interpret and analyze the quality of text embeddings generated by large language models. Topics we will cover…...
Learn Vectorized Thinking in Python Through Examples
2+ week, 4+ day ago (898+ words) Making developers awesome at machine learning In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations. Topics we will cover include: You already know how to…...
Comparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
2+ week, 5+ day ago (703+ words) Making developers awesome at machine learning In this article, you will learn how Gemma 4, Llama 3, and Mistral implement tool calling locally, and what trade-offs each model family presents for real-world deployment. Topics we will cover include: Running AI models locally…...
Integrating Agentic AI with Existing Machine Learning Pipelines
2+ week, 6+ day ago (542+ words) Making developers awesome at machine learning In this article, you will learn how to combine a classical machine learning pipeline with an agentic AI system to build a hybrid, autonomous customer retention workflow. Topics we will cover include: Agentic AI…...
How to Build a Robust RAG System with Minimal Resources
3+ week, 3+ day ago (962+ words) Making developers awesome at machine learning In this article, you will learn how to design, assemble, and tune a retrieval-augmented generation system that runs entirely on a standard laptop, without cloud infrastructure or paid APIs. Topics we will cover include:…...