Category: Intermediate (200)

The NIS2 Directive enhances EU cybersecurity across critical sectors. Learn how Netop provides an integrated solution, run on AWS, that helps customers align with NIS2 Directive goals. Discover how Netop helps address the challenges of NIS2, supporting cybersecurity resilience across critical sectors within the EU.

The blog discusses how Deepgram’s voice AI technology, integrated with AWS services, is modernizing healthcare by reducing administrative burdens on medical professionals. The solution uses Deepgram Nova-3 Medical for accurate medical transcription, Amazon Bedrock for AI orchestration, and Amazon EKS for deployment. Two deployment options are available: self-hosted on AWS or SaaS. Key applications include AI medical scribes, automated prescriptions, logistics enhancement, and intelligent scheduling.

This blog highlights the approach Trellix took to accelerate the development and testing of product integrations and automated rules development. Using agentic AI methodology, Trellix is able to save over 40 hours of development time per integration resulting in a 90% reduction time to market. Trellix used Amazon Bedrock, LangChain, and Anthropic Claude in this effort.

Technology Innovation Institute (TII), an AWS Partner in the Middle East, advances privacy-preserving AI with their enhanced Federated Learning solution, PetalGuard. The solution enables organizations to collaborate on AI model development while maintaining strict data privacy and regulatory compliance. Learn how PetalGuard this development exemplifies TII’s position as a leading technology innovator in the Middle East and demonstrates the value of our strategic collaboration in delivering cutting-edge solutions for privacy-preserving AI development.

Explore how Appian’s AI Copilot, powered by Amazon Bedrock is built directly into the Appian platform to assist both business users and developers in common tasks. For business users, AI CoPilot enables conversational data interaction through Records Chat feature, to help find patterns and trends from real-time data, and provides quick access to business specific documents through the Enterprise Copilot. Whereas, for developers, AI Copilot accelerates application development by assisting in requirements gathering to data modeling, UX design and object generation, while also automating test case creation and generating realistic sample data for testing.

Life sciences organizations invest significantly in real-world data (RWD), yet face persistent challenges in turning these investments into actionable insights. Panalgo’s IHD Cloud platform on AWS addresses these challenges by deploying advanced analytics capabilities directly within customers’ secure environments. Through this approach, organizations have reduced analysis time from months to days while maintaining strict data security and regulatory compliance standards.

Learn how Moneythor’s solution on AWS lowers customer acquisition cost and increases customer engagement through personalization and gamification in financial services by leveraging Amazon EKS, Amazon RDS and Amazon Bedrock.

Learn how to build scalable, real-time data lakes by combining Amazon S3 Tables with Snowflake integration. This solution enables efficient streaming data ingestion and analysis while automatically managing table optimization and maintenance. Discover how manufacturing companies can leverage this architecture to transform IoT sensor data into actionable insights, combining operational analytics with predictive maintenance capabilities. The integration between S3 Tables and Snowflake, along with SageMaker Lakehouse’s Iceberg REST endpoint, creates a robust foundation for streaming analytics that can handle high-volume data streams while maintaining performance and security.

In today’s rapidly changing digital landscape, brands face challenges in delivering consistent personalized customer engagement across multiple communication channels. Discover how the integration between MoEngage’s platform, a leader in marketing analytics and journey orchestration with AWS Communication Developer Services (CDS) enables brands to create seamless and consistent customer experiences through personalized messaging.

The growing demand for generative AI (GenAI) applications has led to a corresponding demand for compute resources that can run these workloads efficiently. In this post we share a step-by-step guide for optimizing GenAI inference workloads using AWS Graviton-based instances. We walk you through downloading Arcee AI SLMs, applying quantization techniques, and deploying models for efficient inference on AWS Graviton instances.