US: At AWS re:Invent, Persistent Systems, a global software and technology developer, and an Advanced Consulting Partner in the Amazon Web Services (AWS) Partner Network, will unveil the first ten Machine Learning (ML) models it has published on AWS Marketplace for Machine Learning. Using Amazon SageMaker, the company has built the models across a variety of domains that include Healthcare, Financial Services, Manufacturing, Astronomy and Smart Cities as well as variety of data types that include image, text and structured data. Some of the models are Breast Cancer Classification, Banking FAQ Intent Matching, Ball Bearing Quality Inspection, Galaxy Classification, and Parking Lot Occupancy Identification.
An example of an organization benefiting from ML is SCIEX, whose mission is to help improve the world we live in by allowing scientists and laboratory analysts to find answers to the complex analytical challenges they face. The company’s global leadership and world-class service and support in capillary electrophoresis and liquid chromatography-mass spectrometry industry have made it a trusted partner to the thousands of scientists and lab analysts worldwide who are focused on basic research, drug discovery and development, food and environmental testing, forensics and clinical research.
“We partnered with Persistent Systems and they delivered a data analytics solution with excellent performance and scalability,” said Bryce Young, Global Manager at SCIEX. “With their deep expertise with analytics and Machine Learning, Persistent helped SCIEX quickly prove a concept and arrive at a decision, all in support of our mission to provide life-changing advancements in analytical tools.”
“With the AWS Marketplace for Machine Learning, we can take the burden off of organizations by reducing the complexity of ML model building,” stated Sameer Dixit, GM Data and ML Practice at Persistent Systems. “We have combined the power of Amazon SageMaker with our unique approach of translating business problems into ML solutions using the right toolsets and experience to democratize ML in an organization making it available to virtually everyone.”