We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Data is precious – so it's been asserted; it has become the world's most valuable commodity. And when it comes to training artificial intelligence (AI) and machine learning (ML) models, it's absolutely essential. Still, due to various factors, high-quality, real-world data can be hard – sometimes even impossible – to come by. This is where synthetic data becomes so valuable.

Today, I am happy to announce that you can now use Amazon SageMaker Ground Truth to generate labeled synthetic image data. Building machine learning (ML) models is an iterative process that, at a high level, starts with data collection and preparation, followed by model training and model deployment. And especially the first step, collecting large, diverse, and accurately labeled datasets for your model training, is often challenging and time-consuming. Let's take computer vision (CV) applications as an example. CV applications have come to play a key role in the industrial landscape.

The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth. Annotators use this tool to label text with named entities and link their relationships, thereby building a dataset for training state-of-the-art natural language processing (NLP) machine learning (ML) models. Most importantly, this is now publicly available to all AWS customers. Booking.com is one of the world's leading online travel platforms. Understanding what customers are saying about the company's 28 million property listings on the platform is essential for maintaining a top-notch customer experience.

Only 9 days until AWS re:Invent 2021, and we're very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders' sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge.

Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for 3D object tracking use cases. Autonomous vehicle (AV) companies typically use LiDAR sensors to generate a 3D understanding of the environment around their vehicles. For example, they mount a LiDAR sensor on their vehicles to continuously capture point-in-time snapshots of the surrounding 3D environment.

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It can reduce your labeling costs by up to 70% using automatic labeling. This blog post explains the Amazon SageMaker Ground Truth chaining feature with a few examples and its potential in labeling your datasets. Chaining reduces time and cost significantly as Amazon SageMaker Ground Truth determines the objects that are already labeled and optimizes the data for automated data labeling mode. As a prerequisite, you might want to check the post "Creating hierarchical label taxonomies using Amazon SageMaker Ground Truth" that shows how to achieve multi-step hierarchical labeling and the documentation on how to use the augmented manifest functionality.