Philippines Seat Facility Achives ISO27001 Accreditation for Secure Collection and Annotation of AI Datasets
SYDNEY – March 7, 2019 – Appen Limited today announced the achievement of ISO/IEC 27001:2013 accreditation for its facility in Cavite, Philippines to meet the rapidly growing demand for human-annotated datasets used by companies worldwide to train machine learning and artificial intelligence (AI) applications.
The facility was established to ensure commercial confidentiality and better support privacy management. Globally recognized, the ISO/IEC 27001:2013 standard defines the requirements for an Information Security Management System (ISMS), and a process-based approach for establishing, implementing, operating, monitoring, maintaining and improving the ISMS.
ISO/IEC 27001:2013 accreditation underpins Appen’s ability to work on datasets for clients that contain personally identifiable information – data that could potentially identify a specific individual – as well as other sensitive material, such as data related to new product development.
“Appen’s facility in the Philippines gives clients enhanced options to scale their AI programs with the assurance that their sensitive data and projects will remain secure,” said Mark Brayan, Appen’s chief executive officer. “It’s the perfect complement to our high-security facility in the U.K., and is an important part of our suite of secure at-home and in-facility solutions.”
Appen works with the world’s leading technology companies, as well as firms across all industries to provide high-quality data for their machine learning programs. Appen’s suite of secure solutions includes secure facilities as well as secure remote worker options designed to scale customers’ machine learning programs while maintaining data privacy. Use case examples range from voice recognition in smart speakers to geospatial analysis in self-driving cars, to intelligent search and online advertising results.
Industry analysts estimate the data collection and annotation market will be worth up to $19 billion – approximately 10% of the overall AI market – by 2025. The explosive growth is attributed to the need for high volumes of annotated data that are required to improve AI algorithm accuracy. Today, approximately one-third of AI applications require frequent or monthly data updates, and one-quarter of those need weekly updates, according to McKinsey Global Institute.
Datasets created by Appen include image, text, audio, video and relevance data, as well as speech and natural language data from over 130 countries and more than 180 languages and dialects.
The announcement today follows Appen’s recent reporting of record results of AU$364.3 million in 2018, representing 119% growth year-over-year.
During the company shareholder call last week, Brayan outlined Appen’s 2019 growth initiatives that include increasing technology development to improve the productivity of its global crowd of over 1 million skilled contractors who develop the AI training datasets in conjunction with Appen’s Client Services team. In addition to expanding Appen’s secure solutions, we also plan to increase its full-time staff and investments in China, the largest AI market outside of the U.S.
Founded in 1996, Appen has office locations worldwide including Seattle, San Francisco, Detroit, and Beijing in addition to the Philippines, U.K. and its headquarters in Sydney.
Off the shelf machine learning datasets repository from Appen. Find 250+ datasets across 80 languages and dialects for a variety of common AI and ML use cases.
Appen is a global leader in the development of high-quality, human-annotated datasets for machine learning and artificial intelligence. Appen brings over 20 years of experience capturing and enriching a wide variety of data types including speech, text, image, and video. With deep expertise in more than 180 languages and access to a global crowd of over 1 million skilled contractors, Appen partners with technology, automotive and e-commerce companies — as well as governments worldwide — to help them develop, enhance and use products that rely on natural languages and machine learning.
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