AWS re:Invent 2018 — AIM302 — Machine Learning at the Edge

Here is my talk about Machine Learning at the Edge:

  • Machine learning at the edge?
  • Leveraging AWS services: Amazon SageMaker, AWS Deep Learning AMI, AWS Greengrass
  • Case study: Toyota Connected Data Services
  • Alternative scenarios if AWS Greengrass isn’t an option
  • Optimizing for inference at the edge
  • Getting started
Illustration for AWS re:Invent 2018 — AIM302 — Machine Learning at the Edge

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About the Author

Julien Simon is the Chief Evangelist at Arcee AI , specializing in Small Language Models and enterprise AI solutions. Recognized as the #1 AI Evangelist globally by AI Magazine in 2021, he brings over 30 years of technology leadership experience to his role.

With 650+ speaking engagements worldwide and 350+ technical blog posts, Julien is a leading voice in practical AI implementation, cost-effective AI solutions, and the democratization of artificial intelligence. His expertise spans open-source AI, Small Language Models, enterprise AI strategy, and edge computing optimization.

Previously serving as Principal Evangelist at AWS and Chief Evangelist at Hugging Face, Julien has authored books on Amazon SageMaker and contributed to the open-source AI ecosystem. His mission is to make AI accessible, understandable, and controllable for everyone.