Redefining AI Infrastructure: Deploying and Developing with a Next-Generation Developer Platform

Tim Davis on a programming language for AI, an efficient, user-friendly inference engine for seamless model execution.

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Tim Davis is the Co-Founder & Chief Product Officer of Modular, a startup focused on building tools to help simplify AI infrastructure.  We discuss two recent announcements:

  • Mojo, a new programming language that combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Mojo is currently accessible via a cloud-based playground that allows for the execution of existing Python code and enables you to convert parts of it into Mojo for significant performance boosts. The language was initially created to facilitate the development of a next-generation machine learning infrastructure stack, capable of scaling machine learning workloads in novel ways.
  • An Inference Engine which executes TensorFlow and PyTorch models with no model rewriting or conversions. It is effectively a compiler and runtime that processes the requests received from the model server and carries out the necessary computations.  Bring your model as-is and deploy it anywhere, across server and edge, with unparalleled usability and performance. Tim describes the strong demand for their inference engine among enterprise customers who commonly use Kubernetes for deployments. He explains the benefits of using this engine, noting that it provides improved performance and efficiency, which subsequently allows customers to build larger models and achieve better accuracy.

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