The OS for AI: How serverless computing enables the next gen of machine learning
Who is this presentation for?
- Data scientists, ML developers and implementers, team leads, and CTOs using ML
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, Ruby, PyTorch, scikit-learn, Caffe, 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 this area’s challenges. Jonathan Peck outlines how the company built, deployed, and scaled thousands of algorithms and machine learning models using every kind of framework. You’ll gain insight 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.
- General knowledge of ML
What you'll learn
- Understand how ML can and must scale in production environments
- Gain a unique take on the evolution of ML as it parallels the early development of operating systems
Colin Spikes, Senior Manager of Solution Engineering at Algorithmia, is an experienced solution consultant with an extensive background in all things data. Prior to Algorithmia, Colin managed a team of Data Solution Architects at Socrata, assisting cities, states, and federal agencies worldwide to unlock the power of data to better understand and communicate conditions in their communities.
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