iMerit to offer secure enterprise-grade data labeling with Amazon Sagemaker ground truth

iMerit, an enterprise-grade data labeling company for Machine Learning (ML) and computer vision, announced a new collaboration with Amazon Web Services (AWS) to provide data labeling services to customers with Amazon SageMaker Ground Truth, a new capability of Amazon SageMaker that makes it easy for customers to efficiently and accurately label the datasets required for training ML systems.

Today, most ML tasks use a technique called supervised learning: an algorithm learns patterns or behaviors from a labeled dataset. Millions of data samples are used in modern ML algorithms to achieve production quality accuracy.

The creation of large and accurate labeled datasets has been a major bottleneck in the journey to scale ML beyond the R&D lab. Amazon SageMaker Ground Truth, announced recently by AWS, addresses this problem with a combination of automated workflows and human intelligence. Customers of Amazon SageMaker Ground Truth have the option to have their data labeled by iMerit, an AWS Partner Network (APN) company that employs over 2,000 in-house data experts in a secure environment to generate data labels consistently and at scale.

"We are excited about this major step in our mission to help Machine Learning reach production scale. The combination of Amazon SageMaker and iMerit's nuanced human annotation at scale, can resolve a traditional bottleneck for customers, and can power their algorithms across various types of data," said Radha Basu, CEO, iMerit. "Enterprise-grade labeling implies quality, scalability, security, and insight, all of which we can jointly offer through this eco-system."

Early access customers have already been working with Amazon SageMaker Ground Truth and iMerit to label image data.

"The rise of AI has transformed how employers source talent and job seekers find work. ZipRecruiter's AI-powered algorithm learns what each employer is looking for and provides a personalized, curated set of highly relevant candidates. On the other side of the marketplace, the company's technology matches job seekers with the most pertinent jobs. And to do all that efficiently, we needed a Machine Learning model to extract relevant data automatically from uploaded resumes," said ZipRecruiter CTO Craig Ogg. "Training a Machine Learning model to be able to identify the most important information requires a sizable dataset to start. The process to create this data is often expensive, manual, and time-consuming. Amazon SageMaker Ground Truth will significantly help us reduce the time and effort required to create datasets for training. Due to the confidential nature of the data, we initially considered using one of our teams but it would take time away from their regular tasks and it would take months to collect the data we needed. Using Amazon SageMaker Ground Truth, we engaged iMerit, a professional labeling company that has been pre-screened by Amazon, to assist with the custom annotation project. With their assistance we were able to collect thousands of annotations in a fraction of the time it would have taken using our own team."

Currently the service supports text classification, image classification, object detection and semantic segmentation. Amazon SageMaker Ground Truth will label the training content (images, audio, text, etc.) by guiding an iMerit labeler step-by-step in a process.

"Many companies and organizations need to train Machine Learning models using their own data, but preparing the datasets is time consuming and expensive. Amazon SageMaker Ground Truth is a managed service that provides a simpler and faster method to get labeled data," said Swami Sivasubramanian, Vice President of Machine Learning, Amazon Web Services, Inc. "iMerit is a trusted APN Partner offering a team of trained specialists who can help customers accurately and securely label the datasets required for training Machine Learning systems. With the integration of iMerit, we are excited about making the process of preparing their training datasets even faster and easier.