Difference between revisions of "DeepLens - deep learning enabled video camera"
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* [http://aws.amazon.com/deeplens/ Product page...] | * [http://aws.amazon.com/deeplens/ Product page...] | ||
| + | ** [http://aws.amazon.com/getting-started/tutorials/ Getting Started Tutorials] | ||
** [http://aws.amazon.com/deeplens/community-projects/ Community Projets] | ** [http://aws.amazon.com/deeplens/community-projects/ Community Projets] | ||
* [http://www.slideshare.net/AmazonWebServices/aws-deeplens-workshop-building-computer-vision-applications AWS DeepLens Workshop: Building Computer Vision Applications] | * [http://www.slideshare.net/AmazonWebServices/aws-deeplens-workshop-building-computer-vision-applications AWS DeepLens Workshop: Building Computer Vision Applications] | ||
Revision as of 13:30, 8 June 2018
Integrated Components
- Lex - conversational interfaces using voice and text
- SageMaker - build, train, and deploy
- Polly - text to speech
- Rekognition - video analysis service
- Kinesis - collect, process, and analyze real-time, streaming data
- Lambda - run code without managing servers
- Greengrass - connected devices can run AWS Lambda functions, keep device data in sync
- Intel® Compute Library for Deep Neural Networks (clDNN) & OpenVINO - deep learning primitives for computer vision
- Simple Queue Service (SQS) - message queuing
- Simple Notification Service (SNS) - pub/sub messaging and mobile notifications
- Internet of Things (IoT) Core - process and route those messages to AWS endpoints
- DynamoDB - NoSQL database
- Simple Storage Service (S3) - object storage
- Management Console - manage web services
Frameworks
Projects
- aws-ml-vision-end2end - Jupyter Notebook tutorials walking through deep learning Frameworks (MXNet, Gluon) to Platforms (SageMaker, DeepLens) for common CV use-cases | GitHub
- Deploy Gluon models to AWS DeepLens using a simple Python API
Processor
With over 100 GFLOPS of compute power on the device, it can process deep learning predictions on HD video for real time.