Artificial Intelligence
NeurIPS competition tackles climate data challenges
The Earth’s climate is a highly complex, dynamic system. It is difficult to understand and predict how different climate variables interact. Finding causal relations in climate research today relies mostly on expensive and time-consuming model simulations. Fortunately, with the explosion in the availability of large-scale climate data and increasing computational power via the cloud, there […]
Interpreting 3D seismic data automatically using Amazon SageMaker
Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. Amazon SageMaker and Apache MXNet on AWS can automate horizon picking using deep learning techniques. In this post, I use these services to build and train a custom deep-learning model for the interpretation of geological features on […]
Standard Voices in Amazon Polly now available in Middle East and Asia Pacific Regions
Amazon Polly turns text into lifelike speech, which allows you to create voice-enabled applications. AWS is excited to announce the general availability of all standard voices in the Middle East (Bahrain) and Asia Pacific (Hong Kong) Regions. Customers in these Regions can now synthesize over 60 standard voices available in 29 languages in the Amazon […]
Cinnamon AI saves 70% on ML model training costs with Amazon SageMaker Managed Spot Training
Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute intensive, keeping compute costs low for […]
Building machine learning workflows with AWS Data Exchange and Amazon SageMaker
Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant inspection grades and scores from AWS […]
Building a custom classifier using Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in texts. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; and understands how positive or negative the text is. For more information about everything Amazon Comprehend can do, […]
Using Amazon Lex Conversation logs to monitor and improve interactions
July 2024: The solution in this blog post is now obsolete with the release of Amazon Lex V2. As a product owner for a conversational interface, understanding and improving the user experience without the corresponding visibility or telemetry can feel like driving a car blindfolded. It is important to understand how users are interacting with your […]
Amazon Textract becomes PCI DSS certified, and retrieves even more data from tables and forms
Amazon Textract automatically extracts text and data from scanned documents, and goes beyond simple optical character recognition (OCR) to also identify the contents of fields and information in tables, without templates, configuration, or machine learning experience required. Customers such as Intuit, PitchBook, Change Healthcare, Alfresco, and more are already using Amazon Textract to automate their […]
Running distributed TensorFlow training with Amazon SageMaker
TensorFlow is an open-source machine learning (ML) library widely used to develop heavy-weight deep neural networks (DNNs) that require distributed training using multiple GPUs across multiple hosts. Amazon SageMaker is a managed service that simplifies the ML workflow, starting with labeling data using active learning, hyperparameter tuning, distributed training of models, monitoring of training progression, […]
Auto-segmenting objects when performing semantic segmentation labeling with Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning (ML) quickly. Ground Truth offers easy access to third-party and your own human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Ground Truth can lower your labeling costs by up to 70% using automatic labeling, […]








