There are many resources to help you get started with machine learning and deep learning. From hands-on tutorials highlighting pretrained models to accessible deep learning frameworks, AI practitioners have numerous tools to add to their workflow. However, when scaling up to larger training datasets and deployment scenarios, the path is not always clear.
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems from a practitioner’s point of view. Along the way, Jason dives deep into available tools, resources, and venues for getting started without having to go it alone.
Jason Knight is senior technology officer at Intel, where he advances what is possible with machine learning using Intel Nervana. Jason holds a PhD in computational biology. His research included developing hierarchical Bayesian statistical models for classification of cancer tumor expression data and high-performance Markov chain Monte Carlo techniques to discover gene regulatory networks in this data using Bayesian networks. He then applied these techniques on the world’s largest database of human genomes at Human Longevity Inc.
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