AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resourcesContinue Reading
Organizations face the challenge to manage data, multiple artificial intelligence and machine learning (AI/ML) tools, and workflows across different environments, impacting productivity and governance. A unified development environment consolidates data processing, model development, and AI application deployment into a single system. This integration streamlines workflows, enhances collaboration, and accelerates AIContinue Reading
Amazon SageMaker Inference has been a popular tool for deploying advanced machine learning (ML) and generative AI models at scale. As AI applications become increasingly complex, customers want to deploy multiple models in a coordinated group that collectively process inference requests for an application. In addition, with the evolution ofContinue Reading
This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services and solutions to align strategically withContinue Reading
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 to adapt to shifting behaviors andContinue Reading
When implementing machine learning (ML) workflows in Amazon SageMaker Canvas, organizations might need to consider external dependencies required for their specific use cases. Although SageMaker Canvas provides powerful no-code and low-code capabilities for rapid experimentation, some projects might require specialized dependencies and libraries that aren’t included by default in SageMakerContinue Reading
As AWS environments grow in complexity, troubleshooting issues with resources can become a daunting task. Manually investigating and resolving problems can be time-consuming and error-prone, especially when dealing with intricate systems. Fortunately, AWS provides a powerful tool called AWS Support Automation Workflows, which is a collection of curated AWS SystemsContinue Reading
Foundational models (FMs) and generative AI are transforming how financial service institutions (FSIs) operate their core business functions. AWS FSI customers, including NASDAQ, State Bank of India, and Bridgewater, have used FMs to reimagine their business operations and deliver improved outcomes. FMs are probabilistic in nature and produce a rangeContinue Reading
Organizations are often inundated with video and audio content that contains valuable insights. However, extracting those insights efficiently and with high accuracy remains a challenge. This post explores an innovative solution to accelerate video and audio review workflows through a thoughtfully designed user experience that enables human and AI collaboration.Continue Reading
Digital pathology is essential for the diagnosis and treatment of cancer, playing a critical role in healthcare delivery and pharmaceutical research and development. Pathology traditionally relies heavily on pathologist expertise and experience to conduct meticulous examination of tissue samples to identify abnormalities. However, the increasing complexity and volume of casesContinue Reading
