As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.