Appen Training Data Solution Unveils Feature Enhancements to Accelerate Customers’ AI Initiatives

Humans and Technology Working Together Enable Better AI Results

Appen Limited, the leading provider of datasets used by companies and governments to train AI systems quickly and at scale, introduced feature updates for its AI training data solution designed to accelerate customers’ artificial intelligence initiatives.

The Appen platform already the most comprehensive solution for collecting and labeling images, text, speech, audio, and video combines Figure Eight’s machine learning (ML)-enabled annotation tools[1] and self-serve client workspaces with Appen Connect to oversee Appen’s global multilingual crowd of more than 1 million skilled contractors, and a wide range of managed services to ensure delivery of high-quality training data at scale, with the speed and security required by customers.

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The feature enhancements today announced include:

• ML-Assisted Text Annotation
• ML-Assisted Text Utterance Collection
• Enterprise Analytics

Available for multiple use cases, these feature updates—together with the platform’s ML-Assisted Video Object Tracking using Dots, Lines, and Polygons capability—further cement Appen’s unique ability to deliver on the increasing volume, quality, and speed requirements for training data to support the world’s most innovative AI systems.

“AI can’t function and improve without a constant stream of large volumes of high-quality training data, a market that will be worth up to $19 billion – 10% of the overall AI market – by 2025,” said Appen CTO Wilson Pang. “To ensure our customers can continue to develop accurate, powerful AI products and services, we are constantly enhancing our solution with new features to help them meet their data needs today and into the future.”

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The new updates focused on text and speech data, enable customers to develop, enhance, and obtain quality training data for their specific AI project or business needs. The updates include:

• Machine Learning-Assisted Text Annotation

New text annotation capabilities locate and classify named entity mentions in unstructured text into predefined categories to support entity extraction and span labeling use cases. Users can also now leverage ‘bring your own model’ outputs to accelerate contributor annotations. Machine Learning-Assisted Text Annotation helps natural language processing (NLP) teams scale quality human annotations. 

• Machine Learning-Assisted Text Utterance Collection

Conversational AI customers can now leverage machine learning validators to collect unique and high-quality text utterances in their domain of choice. Text utterance collection jobs allow customers to leverage the power of a distributed workforce of fluent annotators to collect text strings based on prompts or scenarios to power your conversational agents.

• Enterprise Analytics

Enterprise Analytics provides in-platform reporting on your organization’s usage. For larger enterprises, Enterprise Analytics supports the management of multiple teams across the platform. Organization and team administrators can view in-depth analytics and are then empowered to make data-driven decisions about allocation, resourcing, and ROI.

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