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)
Read More
Shared by AWS Machine Learning January 14, 2022
Recommendation systems are one of the most widely adopted machine learning (ML) technologies in real-world applications, ranging from social networks to ecommerce platforms. Users of many online systems rely on recommendation systems to make new friendships, discover new music according to suggested music lists, or even make ecommerce purchase decisions
Read More
Shared by AWS Machine Learning January 13, 2022
In deep learning, batch processing refers to feeding multiple inputs into a model. Although it’s essential during training, it can be very helpful to manage the cost and optimize throughput during inference time as well. Hardware accelerators are optimized for parallelism, and batching helps saturate the compute capacity and often
Read More
Shared by AWS Machine Learning January 13, 2022
Genome sequencing can help us better understand, diagnose and treat disease. For example, healthcare providers are increasingly using genome sequencing to diagnose rare genetic diseases, such as elevated risk for breast cancer or pulmonary arterial hypertension, which are estimated to affect roughly 8% of the population. At Google Health, we’re
Read More
Shared by Google AI Technology January 13, 2022
Cloud security at AWS is the highest priority. Amazon SageMaker Studio offers various mechanisms to protect your data and code using integration with AWS security services like AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS), or network isolation with Amazon Virtual Private Cloud (Amazon VPC). Customers
Read More
Shared by AWS Machine Learning January 12, 2022
This is the first in a two-part blog series on how Tyson Foods, Inc., is utilizing Amazon SageMaker and AWS Panorama to automate industrial processes at their meat packing plants by bringing the benefits of artificial intelligence applications at the edge. In part one, we discuss an inventory counting application
Read More
Shared by AWS Machine Learning January 11, 2022
Cybersecurity continues to be a top concern for enterprises. Yet the constantly evolving threat landscape that they face makes it harder than ever to be confident in their cybersecurity protections. To address this, ReliaQuest built GreyMatter, an Open XDR-as-a-Service platform that brings together telemetry from any security and business solution, whether on-premises or in one or
Read More
Shared by AWS Machine Learning January 10, 2022
This is a guest post by Jihye Park, a Data Scientist at MUSINSA. MUSINSA is one of the largest online fashion platforms in South Korea, serving 8.4M customers and selling 6,000 fashion brands. Our monthly user traffic reaches 4M, and over 90% of our demographics consist of teens and young
Read More
Shared by AWS Machine Learning January 10, 2022
There is a bit of a philosophical divide in KM circles – those who take a top-down approach to implementation, and those who prefer bottom-up. The truth is that neither are right. Image from wikimedia commons The bottom-up view is that if you provide people with the KM tools, then
Read More
Shared by Nick Milton January 10, 2022
With the advent of artificial intelligence (AI) and machine learning (ML), customers and the general public have become increasingly aware of their privacy, as well as the value that it holds in today’s data-driven world. Enterprises are actively seeking out and marketing privacy-first solutions, especially in the Computer Vision (CV)
Read More
Shared by AWS Machine Learning January 7, 2022
English
العربية
简体中文
Français
Deutsch
한국어
Español
Tiếng Việt
Shared by 
