Explore the AWS machine learning platform using Amazon SageMaker (Day 2)

Outline

Day 1

• Introduction to the Amazon data and ML platform
• Hands-on exercises: Set up an AWS Account; run examples from Amazon ML API services (Transcribe, Translate, Lex, Polly, Comprehend, Rekognition, and more); build an end-to-end ML application with the API services inside SageMaker’s Jupyter notebook
• Introduction to deep learning and SageMaker
• Hands-on exercises: Use SageMaker to build, train, and deploy a machine learning model to the cloud; modify your model to retrain with a larger dataset with a GPU

Day 2

• Running common deep learning frameworks like PyTorch, MxNet, TensorFlow, and Caffe 2 on AWS
• Hands-on exercises: Register your device; create a DeepLens project using the object detection template; deploy the model to the device; check the output on the IoT console and on the video stream
• Architecture group discussion
• Hands-on exercises: Modify your model to focus on new objects using SageMaker; deploy the new model to the device; build an end-to-end application using Lambda, SNS, and other AWS services