Machine Learning

It’s crucial to balance accuracy and efficiency when labeling datasets for machine learning—especially when LLMs are involved. In this article we explore a variety of techniques and assess the optimal labeling methods for different projects.

Machine learning models are trained on massive datasets in which each data point is labeled to give it context and meaning. This deep dive describes how to build a data labeling architecture from scratch, with a focus on workflow, security, and data quality.

With proper instruction, LLMs can be highly effective coding assistants. This step-by-step guide shows you how to generate a call to an external API using Python and the OpenAI API.

Image labeling can be a tedious, time-consuming task, compounded by the sheer volume of data needed to train deep neural networks. This article breaks down large data set processing and explains how a new SaaS product can help automate image labeling.

In this ask-me-anything-style Q&A, leading Toptal AI developer Joao Diogo de Oliveira fields questions from fellow engineers about resources for pivoting to ML, approaches to large language models, and the most critical future applications of AI.

Generative AI is taking the world by storm, with potentially profound impacts on the content we create. Learn the basics of AI image generation and produce sophisticated artistic renderings with this tutorial.

Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here’s how to amplify your data insights with comparison metrics, including the F-measure.

Two heads are better than one. This proverb describes the concept behind ensemble methods in machine learning. Let’s examine why ensembles dominate ML competitions and what makes them so powerful.

Harnessing the potential of machine learning for computer vision is not a new concept but recent advances and the availability of new tools and datasets have made it more accessible to developers.

In this article, Toptal Software Developer Teimur Gasanov demonstrates how you can create an app capable of identifying handwritten digits in under 30 minutes, including the API and UI.