Machine learning has been advancing rapidly, but only a few contributors are focusing on the infrastructure and scaling challenges that come with it. When you have thousands of model versions, each written in any mix of frameworks (Python, R, Java, and Ruby, PyTorch, SciKit, Caffe, and TensorFlow, etc.), it’s difficult to know how to efficiently deploy them as elastic, scalable, secure APIs with 10 ms of latency and GPU access.
Algorithmia has seen many of the challenges faced in this area. Jonathan Peck explores how the company built, deployed, and scaled thousands of algorithms and machine learning models using every kind of framework. You’ll learn some insights into the problems you’re likely to face and how to approach solving them. Jonathan examines the need for, and implementations of, a complete operating system for AI: a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable, and sharable.
A full-stack developer with two decades of industry experience, Jon Peck constantly strives to make technical concepts digestible — demonstrating the value of new technology at every level, from developers through execs.
Former speaker at DeveloperWeek, OSCON, AI Next, O’Reilly AI, ODSC, API World. Former developer/advocate at Mass General Hospital, Cornell University, Algorithmia. Current technical advocate at GitHub.
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