Collect millions of high-quality data samples to ensure your product meets the needs of your customers worldwide
For your machine learning-based solution to correctly recognize images and video, it needs to be trained with enough of those specific data types. While public datasets are available, often they are not specific enough to address your needs nor do they have the right volume to effectively train your algorithm.
We work closely with our clients to develop a customized program to meet their specific needs, and can quickly recruit large numbers of participants for data collection projects. We can meet a variety of requirements for diversity in participant demographics, background visuals and more, and our skilled project managers ensure quality results for each data collection project.
With over 20 years in the industry, Appen has worked with leaders in the technology industry to collect the high-quality data needed to improve their solutions. With access to an experienced crowd of over 1 million people worldwide, and a team of experienced project managers, we can quickly scale your data collection efforts.
The Benefits of Artificial Intelligence are Enhancing the Business Landscape
The unstoppable march of Artificial Intelligence (AI) and machine learning is already touching our lives in so many ways. But its effects have only just begun to take hold.
An Introduction to Machine Learning Training Data [White Paper]
When it comes to your AI strategy, have you considered the amount and type of data you’ll need to effectively train your machine learning models? This white paper aims to help business executives embarking on—or looking to improve—their machine learning initiatives, and covers why machine learning requires a high volume of data, the importance of high-quality data, and what data sources to consider.
Got Data? The Importance of High-Quality Data for Building Effective Machine Learning-Based Solutions [AI Trends Webinar]
Watch this webinar for key insights on how to collect data for machine learning, including pros and cons and trade offs that come with different approaches. When it comes to annotating data for academic purposes, there are specific industry standards that are commonly used. However, when it comes to the commercial sector, building a solution that relies on machine learning …