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Can serverless fix fintech’s scaling problem?

CIO

With serverless components, there is no need to manage infrastructure, and the inbuilt tracing, logging, monitoring and debugging make it easy to run these workloads in production and maintain service levels. Financial services unique challenges However, it is important to understand that serverless architecture is not a silver bullet.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

Architecting a multi-tenant generative AI environment on AWS A multi-tenant, generative AI solution for your enterprise needs to address the unique requirements of generative AI workloads and responsible AI governance while maintaining adherence to corporate policies, tenant and data isolation, access management, and cost control.

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Boost productivity by using AI in cloud operational health management

AWS Machine Learning - AI

Create business intelligence (BI) dashboards for visual representation and analysis of event data. For instance, programmatic rules for event attribute-based noise filtering lack flexibility when faced with organizational changes, expansion of the service footprint, or new data source formats, leading growing complexity.

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Can Dynamic Sites Go Serverless?

Netlify

Why I migrated my dynamic sites to a serverless architecture. Like most web developers these days, I’ve heard of serverless applications and Jamstack for a while. The idea of serverless for a tool that is mostly static content is appealing. Not the usual serverless migration. So, should I migrate at all?

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Dynamic Data Processing Using Serverless Java With Quarkus on AWS Lambda (Part 1)

Dzone - DevOps

With the growth of the application modernization demands, monolithic applications were refactored to cloud-native microservices and serverless functions with lighter, faster, and smaller application portfolios for the past years.

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This is the beginning of the unbundled database era

TechCrunch

Ethan Batraski is a partner at Venrock and focuses on data infrastructure, open source and developer tools. Thanks to the cloud, the amount of data being generated and stored has exploded in scale and volume. As a result, enterprises, on average, store data across seven or more different databases.

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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

Model Context Protocol (MCP) aims to standardize how these channels, agents, tools, and customer data can be used by agents, as shown in the following figure. Developed by Anthropic as an open protocol, the MCP provides a standardized way to connect AI models to virtually any data source or tool.