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Using ML to Optimize ML with Luis Ceze of OctoML | Utilizing AI: 2×14

Training and optimizing a machine learning model takes a lot of compute resources, but what if we used ML to optimize ML? Luis Ceze created Apache Tensor Virtual Machine (TVM) to optimize ML models and has now founded a company, OctoML, to leverage this technology. Fundamentally, machine learning relies on linear algebra, but how should we pick the fastest approach for each model? Today this is done with human intuition, but TVM builds machine learning models to predict the best approaches to try. It also creates an executable so the model can run best on various target hardware platforms. It can also help select the right target platform for a given model.

Three Questions:
  1. How long will it take for a conversational AI to pass the Turing test and fool an average person?
  2. Is machine learning a product or a feature?
  3. Can you think of any fields that have not yet been touched by AI?

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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