A new AI capability that delivers analysis-ready Media Intelligence. More than just a product launch, this is a shift in how communications teams monitor, understand and act on media coverage.
Amazon Web Services offers a complete set of infrastructure and application services that enable you to run virtually everything in the cloud: from enterprise applications and big data projects to social games and mobile apps. Explore how millions of customers — including the fastest-growing startups, largest enterprises, and leading government agencies — are using AWS to lower costs, become more agile, and innovate faster. Source
AWS for Industries When you need to review a complex breast tomosynthesis study containing hundreds of medical images, every second counts. Traditional PACS infrastructure often struggles with storing petabyte-scale imaging data while delivering instant access across distributed care teams.
As organizations embrace cloud-native architectures, the need for agile and scalable integration solutions grows. To grow and innovate at speed, customers need flexible integration solutions with built-in security. MuleSoft, an enterprise integration platform from Salesforce, helps organizations with digital transformation.
AWS Partner Network (APN) Blog By: Syed Sabih ur Rehman, Pre-Sales Solutions Architect – Emumba By: Ore Okebukola, Partner Solutions Architect – AWS If you’re building an AI-powered software as a service (SaaS) product, you’ve likely watched your AI infrastructure costs grow faster than your revenue. What starts at $500 per month during prototyping can reach $50,000 per month in production.
By: Andre Boaventura, Principal AI Solutions Consultant, Applied AI – Pegasystems Inc. By: Surender Kumar, Mainframe Modernization Architect – Pegasystems Inc.
AWS CloudFormation helps you model and provision cloud infrastructure as code using JSON or YAML templates, or through tools like the AWS Cloud Development Kit (CDK). Whether you create stacks directly, use change sets for preview, or deploy through CI/CD pipelines and AI agents, the speed of your deployment cycle directly impacts how fast you can iterate.
As organizations migrate their Oracle databases to Amazon Relational Database Service (Amazon RDS) for Oracle, one critical operational practice that must be re-established in the cloud is safely refreshing non-production environments with production-like data. On-premises, this was a standard process: clone production, mask sensitive data, and hand it off to developers and testers.
CRED is a fintech platform built for India’s most creditworthy individuals. The platform enables users to manage and pay credit card bills, utility bills, rent, and other payments through a single app. CRED manages over 120 production database clusters on Amazon Relational Database Service (Amazon RDS) and Amazon Aurora, with an average switchover time of 2 minutes and a 100 percent operation success rate.
Social engineering through phishing remains one of the most common tactics for launching cyberattacks. AI-generated phishing email messages now pose a new challenge for security teams managing email systems, significantly raising the risk because of their advanced sophistication. Modern social engineers use generative AI and open source intelligence (OSINT) to craft thousands of unique messages with perfect grammar, appropriate context, and personalized details.
By: Len Gomes, Partner Solutions Architect – AWS By: Ruhisar Tikoo, Technical Account Manager – AWS By: Writom Guha Roy, Sr. Startup SA – AWS By: Prateek Chaudhury, AI Engineer – DevRev Enterprises are embedding AI agents across workflows from customer support to IT service management but face the critical challenge of facilitating responsible behavior at scale.
Training a multi-turn agent in Amazon SageMaker AI to resolve support tickets or moderate content means handling a sequence of dependent steps, not a single response. These agents read instructions, make tool calls, read the results, decide the next action, and recover from a mistake before committing to an answer. That flexibility is also what makes agentic reinforcement learning (RL) challenging.