Large language models are transforming how we create, understand our world, and how we work. This is the guide you need to understand what they are and how you can use these models to unlock the power of your data and accelerate your business.

Large Language Models

Guide to Large Language Models | Scale AI

Understand what computer vision is, how computer vision works, and deep dive into some of the top use cases or applications for computer vision by industry. 

Computer Vision Applications

Guide to Computer Vision

Artificial intelligence is a critical part of accelerating digital transformation in eCommerce and Retail. eCommerce companies are now using AI to create new forms of customer engagement, enhance online checkout solutions, and drive cost-effective processes for digital commerce. 

AI for eCommerce

Guide to AI for eCommerce | Scale AI

Guide to AI for eCommerce

Training and building machine learning models enables computers to perform tasks that would be difficult or impossible for them to do without explicit instructions. In the field of computer vision, machine learning models can be trained to recognize and classify objects in images and videos, which has numerous practical applications, such as self-driving cars and security systems. In natural language processing, machine learning models can be used to understand and generate human language, enabling applications such as language translation and chatbots. Learn about the data types, models, optimizers, and infrastructure involved in training ML models.

Training ML Models

Training and Building Machine Learning Models | Scale AI

Training and Building Machine Learning Models

With the Release of Dall-E 2, Google’s Imagen, Stable Diffusion, and Midjourney, diffusion models have taken the world by storm, inspiring creativity and pushing the boundaries of machine learning. In this guide we help to denoise diffusion models, describing how they work and discussing practical applications for today and tomorrow.

Diffusion Models: A Practical Guide | Scale AI

Diffusion Models: A Practical Guide

Data labeling is one of the most critical activities in the machine learning lifecycle, though it is often overlooked in its importance. Powered by enormous amounts of data, machine learning algorithms are incredibly good at learning and detecting patterns in data and making useful predictions, all without being explicitly programmed to do so. Data labeling is necessary to make this data understandable to machine learning models.

Products

Solutions

Resources

Company

Guides