Machine Learning Mastery

This article explains why large context windows are not the same as agent memory, and how retrieval, compression, and summarization techniques fit together in an agent’s cognitive stack.

Learn how to build a text clustering pipeline using large language model embeddings and HDBSCAN to automatically discover topics in unlabeled text data. Covers embedding generation with sentence-transformers, dimensionality reduction with UMAP, and clustering with HDBSCAN.

This article explains how to build AI agents that can browse and interact with real websites using Playwright, browser-use, and LangGraph. It covers Playwright's advantages over Selenium (30-50% faster, persistent WebSocket, built-in auto-waiting, realistic events), setup steps, dynamic page scraping, multi-step form filling, anti-bot detection handling, session persistence, and Docker deployment. Through code examples, readers will create a working browser agent that navigates sites, fills forms, extracts structured data, and uses an LLM for decision-making.

Learn how to build a sentiment analysis pipeline using Scikit-LLM and open-source LLMs served through the Groq API, from setup to evaluation on the IMDB dataset.

From local experiments to production AI systems, mastering key Python concepts is essential.

This article demonstrates multi-label text classification using Scikit-LLM and large language models without labeled training data. It leverages Groq's free open-source LLM for zero-shot inference, using a scikit-learn-like workflow. Steps include setup, classifier initialization, loading the go_emotions dataset, and running predictions that assign multiple sentiment labels to single texts.

This tutorial shows how to build multimodal AI applications — image classification, image captioning, and speech transcription — that run entirely in the browser using Transformers.js, with no server or API key, ensuring user privacy. It includes detailed code examples and project structure.

AgentOps is the operational framework for autonomous AI agents in production, covering observability, evaluation, cost governance, safety, and continuous improvement. This guide explains how AgentOps differs from traditional LLM monitoring, surveys the tooling ecosystem, provides a full working code example, and shows how to debug agent failures using session replay.

Learn how to perform text classification using locally hosted open-source LLMs like Llama 3, Mistral, and Gemma via Ollama and the Scikit-LLM Python library, all without API costs.

This article benchmarks three text classification approaches: TF-IDF with logistic regression, zero-shot BART, and scikit-LLM with a Groq-hosted LLM. On a synthetic customer support dataset, scikit-LLM achieves the highest accuracy (87%) while being faster than BART, making it ideal for small datasets that require deep linguistic understanding.

A structured six-step LLMOps roadmap covering observability, evaluation, cost control, and agent orchestration to build production-grade LLM systems. The LLMOps market is projected to grow from $1.97 billion in 2024 to $4.9 billion by 2028 at a 42% CAGR.

This article demonstrates how to implement a context pruning pipeline for long-running AI agents to manage conversational memory efficiently using semantic similarity. It covers using sentence transformer embedding models, computing similarities, and assembling a pruned context window.

This article provides a detailed walkthrough of how logits, temperature, and top-p sampling work together to control next-token prediction in large language models. It explains the role of logits as raw scores, how temperature and top-p shape the probability distribution, and how they form a sequential pipeline. Practical advice on choosing parameter values for different use cases is also provided.

This article teaches how to transform a basic tool-calling script into a resilient agent that gracefully handles failures from misbehaving tools, malformed model outputs, and unavailable services. Topics include an iterative agent loop with a safety cap, four categories of tool-calling failures, and designing informative error messages for model recovery.

This article explains how to implement a hybrid search strategy for RAG systems by combining BM25 lexical search with semantic search and fusing rankings using Reciprocal Rank Fusion (RRF). It provides step-by-step Python code, including dataset loading, BM25 and semantic search functions, and the hybrid search integration. Experiments on a small dataset show reasonable results, outperforming either method alone.

This article walks through building a context-aware semantic search engine that combines embedding-based similarity with structured metadata filtering, covering everything from generating embeddings to persisting the index.

Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs. This article discusses using statistical guardrails to monitor and evaluate their behavior, ensuring reliability and safety.

This article explains Agentic RAG (Retrieval-Augmented Generation) at three difficulty levels: beginner, intermediate, and advanced. It covers the basic concept, technical architecture, and cutting-edge research, helping readers understand how this approach enhances traditional RAG with autonomous decision-making.

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 — an indispensable element of RAG systems.

Learn to build production-ready AI agents in Python using Pydantic AI, with structured outputs, custom tools, and dependency injection.

This article explores context engineering for AI agents, focusing on treating the context window as a constrained resource, separating static and dynamic context, managing conversation history, designing retrieval as a budget decision, and evaluating context quality in production.

This article explains how to use Scikit-LLM's text summarization feature to handle large volumes of text in machine learning pipelines. It covers building a custom Hugging Face summarizer transformer, integrating it into a scikit-learn pipeline with TF-IDF vectorization and a classifier, and demonstrates the process with code examples.

This article explains how to build a fully functional AI agent that runs locally on your machine using small language models, with no internet connection or API costs. It covers the concepts of AI agents and SLMs, the advantages of local deployment, setting up Ollama and Python libraries, step-by-step agent construction, adding memory and tools, and discusses the limitations of SLMs.

This guide walks through training a Scikit-learn classifier, building a FastAPI inference server, testing it locally, and deploying it to FastAPI Cloud. It uses the breast cancer dataset and a RandomForest model.

This article explains AI agent memory across three difficulty levels: the fundamental memory problem in stateless LLM agents, the main memory types (in-context, external), and scalable architectures including writing strategies, retrieval methods, decay handling, and multi-agent consistency. It provides practical insights for building agents that improve over time.

Zero-shot text classification allows labeling text without task-specific training data by turning labels into natural language statements and using a pretrained model to check if the text supports them. This article covers how it works, using facebook/bart-large-mnli for single and multi-label classification, and improving results with custom hypothesis templates.