In our Community Showcase, Amazon Web Services (AWS) highlights projects created by AWS Heroes and AWS Community Builders.
Each month AWS ML Heroes and AWS ML Community Builders bring to life projects and use cases for the full range of machine learning skills from beginner to expert through deep dive tutorials, podcasts, videos, and other content that show how to use AWS Machine Learning (ML) solutions such as Amazon SageMaker, pertained AI services such as Amazon Rekognition, and AI learning devices such as AWS DeepRacer.
The AWS ML community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and ML concepts, contribute with real-world experiences, and collaborate on building projects together.
Here are a few highlights of externally published getting started guides and tutorials curated by our AWS ML Evangelist team led by Julien Simon.
AWS ML Heroes and AWS ML Community Builder Projects
Making My Toddler’s Dream of Flying Come True with AI Tech (with code samples). In this deep dive tutorial, AWS ML Hero Agustinus Nalwan walks you through how to build an object detection model with Amazon SageMaker JumpStart (a set of solutions for the most common use cases that can be deployed readily with just a few clicks), Torch2trt (a tool to automatically convert PyTorch models into TensorRT), and NVIDIA Jetson AGX Xavier.
How to use Amazon Rekognition Custom Labels to analyze AWS DeepRacer Real World Performance Through Video (with code samples). In this deep dive tutorial, AWS ML Community Builder Pui Kwan Ho shows you how to analyze the path and speed of an AWS DeepRacer device using pretrained computer vision with Amazon Rekognition Custom Labels.
AWS Panorama Appliance Developers Kit: An Unboxing and Walkthrough (with code samples). In this video, AWS ML Hero Mike Chambers shows you how to get started with AWS Panorama, an ML appliance and software development kit (SDK) that allows developers to bring computer vision and make predictions locally with high accuracy and low latency.
Improving local food processing with Amazon Lookout for Vision (with code samples). In this deep tutorial, AWS ML Hero Olalekan Elesin demonstrates how to use AI to improve the quality of food sorting (using cassava flakes) cost-effectively and with zero AI knowledge.
Conclusion
Whether you’re just getting started with ML, already an expert, or something in between, there is always something to learn. Choose from community-created and ML-focused blogs, videos, eLearning guides, and much more from the AWS ML community.
Are you interested in contributing to the community? Apply to the AWS Community Builders program today!
The content and opinions in the preceding linked posts are those of the third-party authors and AWS is not responsible for the content or accuracy of those posts.
About the Author
Cameron Peron is Senior Marketing Manager for AWS Amazon Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.