MACHINE LEARNING FOR EVERYDAY DEVELOPERS
Amazon SageMaker and AWS DeepLens.
From personalized recommendations to the cashier-free Amazon Go grocery store, there is a long heritage of machine learning (ML) at Amazon. While there’s a lot of hype surrounding ML and AI, it’s still very early days for most companies, and skilled ML practitioners are few and far between. To remove the heavy lifting from building ML models from scratch, AWS introduced Amazon SageMaker, which is designed for everyday developers to easily build, train, and deploy ML models.
The fully managed service takes away the complexity of ML implementation by providing 10 commonly-used algorithms, broad framework support, as well as one-click training at petabyte scale, and one-click tuning via hyper-parameter optimization for highest possible accuracy. The resulting ML model can then be deployed on an auto-scaling cluster of EC2 instances across multiple availability zones. In addition, SageMaker also provides oneclick inference, native A/B testing support, and secure HTTPS endpoint for high throughput and low latency predictions.
To help developers realize the potential of ML in a more hands-on manner, AWS also launched DeepLens, which is the world’s first deep learning-enabled wireless video camera. The custom-designed hardware sports a HD video camera, and is capable of running over 100 billion deep learning operations per second. It also comes with sample projects and pretrained models, and is fully programmable with Lambda functions. When integrated with SageMaker, developers can run and deploy trained ML models on the device in real time.