Do you ever wonder what goes behind bringing various levels of autonomy to vehicles? What the vehicle sees (perception) and how the vehicle predicts the actions of different agents in the scene (behavior prediction) are the first two steps in autonomous systems. In order for these steps to be successful, large-scale driving datasets are key. Driving datasets typically comprise of data captured using multiple sensors such as cameras, LIDARs, radars, and GPS, in a variety of traffic scenarios during different times of the day under varied weather conditions and locations. The Amazon Machine Learning Solutions Lab is collaborating with the Laboratory of Intelligent and Safe Automobiles (LISA Lab) at the University of California, San Diego (UCSD) to build a large, richly annotated, real-world driving dataset with fine-grained vehicle, pedestrian, and scene attributes. This post describes the dataset label taxonomy and labeling architecture for 2D bounding boxes using Amazon SageMaker Ground Truth. Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning (ML) workflows. These workflows support a variety of use cases, including 3D point clouds, video, images, and text.

High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it's becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create a consumer-facing application that regularly collects and sends new data objects to a data labeling pipeline, which produces labels and builds a dataset for model training or retraining. This pipeline creates a positive feedback loop that leads to more accurate, sophisticated models. Amazon SageMaker Ground Truth streaming labeling jobs provide infrastructure and resources to create a continuously running labeling job that receives new data objects on demand and sends them to human workers to be labeled. You can chain multiple streaming labeling jobs together to create more intricate and refined data labeling pipelines. Use this blog post to learn how to set up and customize Ground Truth streaming labeling jobs.

Serverless and containers changed the way we leverage public clouds and how we write, deploy, and maintain applications. A great way to combine the two paradigms is to build a voice assistant with Alexa based on Lambda functions -- written in Go -- to deploy a Docker Swarm cluster on AWS. The figure below shows all components needed to deploy a production-ready Swarm cluster on AWS with Alexa. Note: The full code is available on my GitHub. Echo will interpret the user's voice command with built-in natural language understanding and speech recognition, then convey them to the Alexa service.

Chatbot credentials (app id, secret, page tokens, etc…) can be saved to lambda env vars and retrieved when necessary. Whenever your endpoint gets a correctly signed data from a chat provider and it should return immediately one of the 200(ok), 201(created), or 202(accepted) HTTP status codes. If data is not signed correctly or signature is missing, good to return 403(forbidden) and do not move the data to SNS topic. On every event, the event should be published to an SNS topic with the event inputs and outputs. Appropriate lambda functions should subscribe to the predefined SNS topics and process the data.

You can't just assume that when an application reads each letter of a sentence that the output will make sense. Amazon Polly provides speech synthesis functionality that overcomes those challenges, allowing you to focus on building applications that use text-to-speech instead of addressing interpretation challenges. Amazon Polly turns text into lifelike speech. It lets you create applications that talk naturally, enabling you to build entirely new categories of speech-enabled products. Amazon Polly is an Amazon AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.