Data annotation is seeing advances such as using AI and automation that seemingly promise to eliminate the need for human-powered labeling. This panel explores the future of humans-in-the-loop data annotation and what role your labeling workforce will play in the years to come.
Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful ML algorithm. CloudFactory and Keymakr discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and best practices for building your own.
The end goal for every data labeling project is quality data - but how do you get there? There are several QA workflow types but each has pros and cons when it comes to the quality and speed of data outputs. In this panel discussion, we will explore 5 quality assurance workflows for data labeling including tooling, staffing, and how each workflow affects throughput and data quality.
It takes a mountain of data to train, test, and build machine learning algorithms and AI projects. You may be considering hiring a data labeling service to take the burden off your in-house data scientists and machine learning engineers. But what does that entail? Watch the webinar to learn what you need to know before hiring a data labeling service.
Developing high-performance deep learning models for computer vision requires a strategic combination of people, tools, and processes in pre-production. Watch the webinar to learn how to streamline your data labeling and experimentation process to accelerate your ML training and your time to market.
Data-science tech developer Hivemind designed a quantitative experiment to determine which type of workforce completed a series of increasingly complex tasks to deliver the highest-quality structured datasets. Watch the webinar to learn which workforce performed better.