From training new models to deploying them in production, Amazon SageMaker offers the most complete set of tools for startups and enterprises to harness the power of machine learning (ML) and Deep Learning. With its Transformers open-source library and ML platform, Hugging Face makes transfer learning and the latest ML
Read More
Shared by AWS Machine Learning January 20, 2022
Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is
Read More
Shared by AWS Machine Learning January 20, 2022
This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama
Read More
Shared by AWS Machine Learning January 18, 2022
Natural language understanding is applied in a wide range of use cases, from chatbots and virtual assistants, to machine translation and text summarization. To ensure that these applications are running at an expected level of performance, it’s important that data in the training and production environments is from the same
Read More
Shared by AWS Machine Learning January 18, 2022
There are two approaches to managing a body of knowledge within a community of practice – curation, and synthesis. One of the functions of a community of practice is to provide a means for developing and co-owning a documented “body of knowledge” on behalf of the community (the other function
Read More
Shared by Nick Milton January 17, 2022
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
English
العربية
简体中文
Français
Deutsch
한국어
Español
Tiếng Việt
Shared by 