The Roadmap to Mastering AI Agent Evaluation
Let’s not waste any more time.
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vector machines.
AI Agent Tool Design: What Works and What Doesn’t
Most
Python Concepts Every AI Engineer Must Master
Transitioning from writing local experimental scripts to building scalable, production-grade AI systems requires a shift in how we write Python.
Multi-Label Text Classification with Scikit-LLM
Text classification typically boils down to scenarios where a product review is “positive” or “negative”, or a customer inquiry belongs to one category or another.
Multimodal Browser AI with Transformers.js for Images and Speech
Most browser AI tutorials cover text because it is a natural starting point, but the applications people actually want to build are rarely text-only.
The Practitioner’s Guide to AgentOps
According to Futurum Research’s 2025 market overview of agentic AI platforms,
Building Semantic Search with Transformers.js and Sentence Embeddings
You’ve probably shipped this bug before, where a user types ” affordable laptop ” into your search bar and gets zero results.
Using Scikit-LLM with Open-Source LLMs
This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.
Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?
In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .