The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems to deploy in the cloud and at the edge, their goals to ensure the model delivers the best possible functionality and end-user experience- and importantly, their hardware and software stack may require different optimizations to achieve that goal.

OctoML provides a SaaS product called the Octomizer to help developers and AIOps teams deploy ML models most efficiently on any hardware, in any context. The Octomizer deploys its own ML models to analyze your model topology, and optimize, benchmark, and package the model for deployment. The Octomizer generates insights about model performance over different hardware stacks and helps you choose the deployment format that works best for your organization.

Luis Ceze is the Co-Founder and CEO of OctoML. Luis is a founder of the ApacheTVM project, which is the basis for OctoML’s technology. He is also a professor of Computer Science at the University of Washington. Jason Knight is co-founder and CPO at OctoML. Luis and Jason join the show today to talk about how OctoML is automating deep learning engineering, why it’s so important to consider hardware when building deep learning systems, and how the field of deep learning is evolving.

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