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, en...| scale.com
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.| scale.com
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.| scale.com
Nuro mines for rare classes in unlabeled data with Nucleus.| scale.com
At Harvard Medical School, the Datta Lab accelerates their research using Scale Rapid with data labeling and annotation.| scale.com
Learn to label 1M data points/week with scalable workflows and expert-quality annotations.| scale.com
How Large Language Models are Trained and Tuned using Reinforcement Learning with Human Feedback (RLHF).| scale.com
The Scale Data Engine powers large language models (LLMs), generative AI, and computer vision applications with best-in-class data.| scale.com