Michael Abramov

Data annotation is a crucial step in training machine learning algorithms. It involves tagging, transcribing, or labeling specific features in datasets to train the ML system. To achieve accurate and reliable results, it is important to choose the right data annotation platform. This article will highlight the key factors to

Data annotation is a crucial process in training machine learning models. It involves labeling individual elements of training data, such as text, images, audio, or video, to help machines understand what the data contains. Annotated data is used to train models and is also important for quality control in the

In the field of computer vision and machine learning, open-source data annotation tools play a crucial role in accurate AI data labeling and machine learning model training. These tools are freely available for use, making them a popular choice for startups and academic projects with limited budgets. In this article,

Artificial Intelligence (AI) is revolutionizing the way we live and work, and the finance industry is no exception. From fraud detection to portfolio management, AI is transforming the way we manage our finances. In this article, we'll explore the top 5 AI-powered finance apps that are changing the

A Game-Changer in Data Labeling Process and Efficiency In today's data-driven ecosystem, the foundational integrity of datasets stands paramount, and this is where Keylabs has always prided itself. With a rich legacy anchored in the realms of image labeling, Keylabs has persistently been at the forefront of providing

Self-driving vehicles are the future of transportation, and data annotation plays a crucial role in their development. Data annotation is the process of labeling and categorizing data to train machine learning algorithms. In the context of self-driving vehicles, data annotation involves labeling images, videos, and other sensor data to help