Broadcast Date: November 17, 2020

Level: 100

Companies are solving some of the worlds most challenging problems in food production with AI/ML practices, but moving the data from the edge to the cloud can still be a challenge in rural and remote areas. For companies facing these challenges the key design considerations are latency, speed to decision, and model re-training. In this tech talk, we will focus on end to end capabilities in AWS IoT and connectivity patterns based on common customer use cases, sharing key wins and creative forward leaning applications in both commodity production and livestock health.

Learning Objectives

  • Learn how services like AWS IoT Core, AWS IoT Greengrass, and the AWS Snow family of devices enable edge to cloud movement of sensor, time series or imagery data
  • Learn how AWS IoT Greengrass can perform edge inference using containerization, ML Inference, and Amazon SageMaker Neo models
  • Understand the services that come together in the Connected Farm reference architecture

Who Should Attend?

Hardware Engineers, Dev Ops Engineers, Data Scientists, Data Engineers, Agriculture Decision Makers, CTO, CIO, Founder


  • Karen Hildebrand, Tech Leader, Agriculture, AWS
  • Rachel Bradshaw, Sr. IoT GTM Specialist, AWS

Learn More

To learn more about the services featured in this talk, please visit:

Intro body copy here about 2018 re:Invent launches.

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