zetabyte

​[[{“value”:” Crafting unique, customized experiences that resonate with customers is a potent strategy for boosting engagement and fostering brand loyalty. However, creating dynamic personalized content is challenging and time-consuming because of the need for real-time data processing, complex algorithms for customer segmentation, and continuous optimization… Read More »Streamline personalization development: How automated ML workflows accelerate Amazon Personalize implementation Reagan Rosario AWS Machine Learning Blog

​[[{“value”:” Standard operating procedures (SOPs) are essential documents in the context of regulations and compliance. SOPs outline specific steps for various processes, making sure practices are consistent, efficient, and compliant with regulatory standards. SOP documents typically include key sections such as the title, scope, purpose,… Read More »Fast-track SOP processing using Amazon Bedrock Ganesh Raam Ramadurai AWS Machine Learning Blog

​[[{“value”:” Home Table of Contents Content Moderation via Zero Shot Learning with Qwen 2.5 What Is Content Moderation? Content Moderation for Social Media Safety Facebook Hateful Memes Challenge Overview of Qwen 2.5 Vision-Language Models Era of Vision-Language Models Introducing Key Features of Qwen 2.5 Models… Read More »Content Moderation via Zero Shot Learning with Qwen 2.5 Puneet Mangla PyImageSearch

​This post is divided into three parts; they are: • Understanding Word Embeddings • Using Pretrained Word Embeddings • Training Word2Vec with Gensim • Training Word2Vec with PyTorch • Embeddings in Transformer Models Word embeddings represent words as dense vectors in a continuous space, where… Read More »Word Embeddings in Language Models Adrian Tam MachineLearningMastery.com

​[[{“value”:” Amazon SageMaker Projects empower data scientists to self-serve Amazon Web Services (AWS) tooling and infrastructure to organize all entities of the machine learning (ML) lifecycle, and further enable organizations to standardize and constrain the resources available to their data science teams in pre-packaged templates. For… Read More »Deploy Amazon SageMaker Projects with Terraform Cloud Max Copeland AWS Machine Learning Blog

​[[{“value”:” Join Danielle Belgrave and Ben Lorica for a discussion of AI in healthcare. Danielle is VP of AI and machine learning at GSK (formerly GlaxoSmithKline). She and Ben discuss using AI and machine learning to get better diagnoses that reflect the differences between patients.… Read More »Generative AI in the Real World: Danielle Belgrave on Generative AI in Pharma and Medicine Ben Lorica and Danielle Belgrave AI & ML – Radar

​[[{“value”:” ZURU Tech is on a mission to change the way we build, from town houses and hospitals to office towers, schools, apartment blocks, and more. Dreamcatcher is a user-friendly platform developed by ZURU that allows users with any level of experience to collaborate in… Read More »How ZURU improved the accuracy of floor plan generation by 109% using Amazon Bedrock and Amazon SageMaker Federico Di Mattia, Simone Bartoli, Marco Venturelli, Stefano Pellegrini, Enrico Petrucci AWS Machine Learning Blog

​[[{“value”:” Generative AI revolutionizes business operations through various applications, including conversational assistants such as Amazon’s Rufus and Amazon Seller Assistant. Additionally, some of the most impactful generative AI applications operate autonomously behind the scenes, an essential capability that empowers enterprises to transform their operations, data… Read More »Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI Burak Gozluklu AWS Machine Learning Blog

​[[{“value”:” Generative AI applications seem simple—invoke a foundation model (FM) with the right context to generate a response. In reality, it’s a much more complex system involving workflows that invoke FMs, tools, and APIs and that use domain-specific data to ground responses with patterns such… Read More »Architect a mature generative AI foundation on AWS Chaitra Mathur AWS Machine Learning Blog

​[[{“value”:” When ingesting data into Amazon OpenSearch, customers often need to augment data before putting it into their indexes. For instance, you might be ingesting log files with an IP address and want to get a geographic location for the IP address, or you might… Read More »Using Amazon OpenSearch ML connector APIs John Trollinger AWS Machine Learning Blog