Machine Learning Mastery
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Latest publications
Machine Learning Mastery shows how to build RAG on scikit-learn without a vector database
Machine Learning Mastery walks through a lightweight end-to-end RAG pipeline: open-model embeddings, vector search in scikit-learn, and answer generation from retrieved context.
Unsloth and Llama 3: How to Teach a Model to Call APIs Reliably via JSON
Machine Learning Mastery walks through fine-tuning Llama 3 for strict JSON tool calling with Unsloth: a general-purpose model breaks API schemas, and LoRA helps fix that.
How to Build a Browser AI Agent in Python: A Step-by-Step Guide from Machine Learning Mastery
Machine Learning Mastery has published a guide to building Python agents that control a browser: they open sites, click and fill in forms without an official API.
Transformers.js and Embeddings: Semantic Search Without Server Infrastructure
Machine Learning Mastery shows how to replace keyword search with semantic search using Transformers.js, a browser library that builds vector embeddings on the client with no server API.
Agentic AI Pipeline: 7 Components That Separate Production from a Demo
Machine Learning Mastery has published a guide to seven architectural components that set a production agentic AI system apart from an ordinary demo script.
How AI Agents Manage Context Windows in Long Tasks: Five Strategies
Machine Learning Mastery breaks down five practical strategies for managing context windows for AI agents that run for hours and hit model token limits.
LangChain vs LlamaIndex: how developers choose a framework for LLM applications
Developers of LLM applications usually start with direct API calls, but as the codebase grows more complex, they move to specialized frameworks like LangChain or LlamaIndex.
How to Choose Tools for AI Agents: A Complete Guide from Machine Learning Mastery
Machine Learning Mastery published a guide on selecting tools for AI agents: why less is often better and how the quality of tool descriptions impacts agent reliability.
HDBSCAN Clustering with LLM Embeddings: How Noise Labels Work
Machine Learning Mastery shows how to group unstructured text by topic with LLM embeddings and HDBSCAN — without manual labeling or a predefined number of categories.
Technical stack of AI agents: LLM, orchestration, vector memory, and tools
Machine Learning Mastery breaks down the components of a modern AI agent: an orchestration framework, memory layers, tools for taking action, and monitoring.
Context window is not memory: what AI agent developers need to understand
AI agent developers often confuse a large context window with long-term memory — an architectural error that surfaces in production with returning users.
A Roadmap for Evaluating AI Agents: Metrics, Benchmarks, and Practical Methods
Evaluating AI agents is harder than evaluating language models: it requires separate metrics for multi-step tasks, tool use, and error recovery.
Scikit-LLM: an end-to-end text sentiment analysis pipeline with language models
Scikit-LLM embeds GPT and other LLMs into sklearn pipelines—sentiment analysis without data labeling or fine-tuning, in a familiar sklearn interface.
Scikit-LLM: multi-label text classification without a training dataset using LLMs
The Scikit-LLM library makes it possible to assign multiple tags to each text without a training dataset, using GPT-4 and compatible models through a familiar scikit-learn interface.
How to Design Tools for AI Agents: Working Practices and Common Mistakes
Machine Learning Mastery analyzed why an AI agent performs as well as its tools are designed — and which mistakes make them useless.
Machine Learning Mastery: Python Concepts Every AI Engineer Must Master for Production
Machine Learning Mastery explained which Python patterns distinguish experimental scripts from scalable AI systems capable of handling real-world load.
How Token Selection Works in Neural Networks: logits, Temperature, and top-p
Understanding the mathematics of LLM text generation: how logits, temperature, and top-p affect the balance between accuracy and creativity in responses.
Context-pruning for long-lived LLM agents: a memory management technique
Agents based on large language models require a new approach to memory management during long sessions. Context-pruning allows removing unnecessary information and saving tokens.
Hybrid Search in RAG: When Semantics Meet Keywords
Hybrid search combines semantic and lexical algorithms—critical for production-ready RAG systems.
Multi-agent Research Assistant in Python with OpenAI SDK
OpenAI introduced Agents SDK — a framework for building systems of multiple agents that work together to search and analyze information. This opens new possibilities for automating research.
Machine Learning Mastery: Semantic Search with Embeddings Instead of Keywords
Keyword search fails when documents don't contain the exact words users are searching for. Machine Learning Mastery shows how to solve this with LLM embeddings and metadata.
How to choose an AI agent architecture: a decision tree from Machine Learning Mastery
Machine Learning Mastery has published a guide with a decision tree for choosing the optimal AI agent design pattern. The choice depends on the task type, scalability requirements, and the nature of interactions with ext
Machine Learning Mastery explained how to build ML systems without servers and large datasets
Machine Learning Mastery released a practical guide to ML in conditions of limited hardware, poor internet, and messy data — with an emphasis on simple models and straightforward solutions.
Machine Learning Mastery explained how vector databases work from simple to complex
Machine Learning Mastery released a detailed guide to vector databases: from embeddings and similarity search to HNSW, IVF, PQ, and the trade-offs between accuracy, memory, and latency.
LlamaCloud added LlamaAgents Builder for building and deploying AI agents in minutes
LlamaCloud now includes LlamaAgents Builder, a beta service that builds a document-processing agent from a text description, deploys it via GitHub, and lets users test it in the interface.
Machine Learning Mastery highlighted 7 itertools functions for feature engineering in Python
Machine Learning Mastery published a practical breakdown of seven Python itertools functions that help build interaction, lag, polynomial, and cumulative features faster without bulky loops.
Machine Learning Mastery identified 7 ML trends that will shape 2026
Machine Learning Mastery highlighted seven machine learning trends for 2026: agentic systems, generative AI as infrastructure, small models, edge computing, and the growing role of MLOps.
Machine Learning Mastery showed how Python decorators make ML services more reliable
Machine Learning Mastery broke down five Python decorators for production ML: they help withstand API failures, validate inputs, save compute resources, and improve service observability.
Machine Learning Mastery explained how to avoid race conditions in multi-agent systems
Machine Learning Mastery published an analysis of race conditions in multi-agent systems: why agents corrupt shared state without errors and which patterns reduce the risk.
Google’s Gemma 4: how to run tool calling locally with Python and Ollama
Machine Learning Mastery showed how to turn Gemma 4 into a local agent with tool calling: using Ollama and Python, the model calls functions, gets data from APIs, and responds without the cloud.