Machine Learning Mastery
AI news source. Articles are auto-selected and adapted by Hamidun News editors.
Latest publications
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.
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.
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.
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.
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.
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.
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.
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.
Machine Learning Mastery explained how to build long-context RAG without extra tokens
Machine Learning Mastery broke down five techniques for long-context RAG: reranking, caching, hybrid search, metadata, and query expansion to reduce noise, cost, and latency.
Machine Learning Mastery showed how to run zero-shot text classification without a dataset
Machine Learning Mastery released a practical breakdown of zero-shot text classification: how to define categories, use BART, and get labels without training on your own dataset.
Why memory has become a key element of AI agents: a breakdown across three levels of complexity
A new breakdown of memory in AI agents shows the main point: without preserving context, a model responds in isolation, while useful agent systems are built on memory of the dialogue, tasks, and past sessions.
Machine Learning Mastery identified five major barriers to scaling agentic AI in 2026
Machine Learning Mastery compiled five problems preventing agentic AI from transitioning from impressive demos to stable production: from orchestration to security and cost control.
Machine Learning Mastery: why one vector store is not enough for AI applications
Machine Learning Mastery explains why production AI cannot live on vector store alone: SQL layer is also needed for access control, billing, metadata, and application state.