Machine Learning on the Cloud with MXNet
The growing number of uses for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continues to drive the development of cutting-edge technology solutions. Biomedical research and medical care are fields that are poised to be dramatic change as they start to integrate computer vision, predictive modeling, natural language understanding, and recommendation engines within standard practice. In this talk, we will review why AI and ML are hard problems to tackle, describe some cutting edge examples in biomedical research and other industries that are applying these techniques to create materially better solutions, and then dive into the details of the family of intelligent services at AWS that provide cloud-native machine learning and deep learning technologies to address a wide range of research needs. We will focus specifically on deep learning applications and products, such as the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Angel Pizarro leads the global genomics, life science, and precision medicine initiatives within the Research and Technical Computing at Amazon Web Services. He has over 17 years of experience in bioinformatics, supporting a broad range of technologies such as high-throughput sequencing, proteomics, and metabolomics. Prior to joining AWS in 2013, he lead a bioinformatics research team at the University of Pennsylvania School of Medicine, developing systems and algorithms to support a broad set of research problems focused on genomic expression and cardiovascular research.
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