The Tech Radar - Issue #03 (May 2025)

The Dialogues stage at Google I/O 2025 brought together Google leaders and visionaries.

At this week’s I/O, we announced our very latest products, tools and research designed to make AI even more helpful with Gemini. The latest episode of the Google AI: Rel…

From Imagen 4 and Veo 3 to Flow, try these new generative media tools today.

Learn more about the biggest announcements and launches from Google’s 2025 I/O developer conference.

Today at I/O, we showed how we’re enhancing Search with our latest Gemini models via AI Mode.

Learn more about Google’s new shopping features in AI Mode as well as a new virtual try-on tool that works with personal photos.

The Gemini app is getting major new updates, from Veo 3 and Imagen 4 to Deep Research and Canvas.

Colab Enterprise is a collaborative, managed notebook environment with the security and compliance capabilities of Google Cloud. Powerful integrated AI, seamless collaboration tools, enterprise readiness, and zero-config flexible compute are some of the many features making Colab Enterprise a trusted tool for developers at companies of all sizes. Today, we’re excited to announce new productivity boosting capabilities in Colab Enterprise, including: Code assistance powered by Gemini to improve...

The AI era has supercharged expectations: users now issue more complex queries and demand pinpoint results, meaning there's an 82% chance of losing a customer if they can't quickly find what they need. Similarly, AI agents require ultra-relevant context for reliable task execution. However, when traditional search methods deliver noise – with generally up to 70% of retrieved passages lacking a true answer – both agentic workflows and user experiences suffer from untrustworthy and unreliable...

Today, we are introducing the next wave of generative AI media models on Vertex AI: Imagen 4, Veo 3, and Lyria 2. We’ve already seen customers generate stunning, photorealistic images with Imagen 3 , Google’s image generation model. Customers have taken these images and transformed them into high quality videos and assets with Veo 2 . We’ve even seen customers take these remarkable videos and bring them to life with professional-grade audio using Lyria , Google’s advanced AI music generation...

At Google Cloud, we’re committed to providing the most open and flexible AI ecosystem for you to build solutions best suited to your needs. Today, we’re excited to announce our expanded AI offerings with Mistral AI on Google Cloud: Le Chat Enterprise on Google Cloud Marketplace : An AI assistant that offers enterprise search, agent builders, custom data and tool connectors, custom models, document libraries, and more in a unified platform. Mistral OCR 25.

Today, we're expanding the choice of third-party models available in Vertex AI Model Garden with the addition of Anthropic’s newest generation of the Claude model family: Claude Opus 4 and Claude Sonnet 4 . Both Claude Opus 4 and Claude Sonnet 4 are hybrid reasoning models, meaning they offer modes for near-instant responses and extended thinking for deeper reasoning. Claude Opus 4 is Anthropic’s most powerful model to date.

Today, we're excited to announce Google AI Edge Portal in private preview, Google Cloud’s new solution for testing and benchmarking on-device machine learning (ML) at scale. Machine learning on mobile devices enables amazing app experiences. But how will your model truly perform across the vast, diverse, and ever-changing landscape of mobile devices? Manually testing at scale – across hundreds of device types – is a laborious task that often requires a dedicated device lab.

Learn about the new SynthID Detector portal we announced at I/O to help people understand how the content they see online was generated.

Today we introduced Flow, a new AI filmmaking tool built with and for creatives for the next wave of storytelling.

Welcome to the second Cloud CISO Perspectives for May 2025. Today, Enrique Alvarez, public sector advisor, Office of the CISO, explores how government agencies can use AI to improve threat detection — and save money at the same time. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog . If you’re reading this on the website and you’d like to receive the email version, you can subscribe here .

Confidential Computing has redefined how organizations can securely process their sensitive workloads in the cloud. The growth in our hardware ecosystem is fueling a new wave of adoption, enabling customers to use Confidential Computing to support cutting-edge uses such as building privacy-preserving AI and securing multi-party data analytics. We are thrilled to share our latest Confidential Computing innovations , highlighting the creative ways our customers are using Confidential Computing to.

Welcome to the first Cloud CISO Perspectives for May 2025. Today, Iain Mulholland, senior director, Security Engineering, pulls back the curtain on how Google Cloud approaches security engineering and how we take secure by design from mindset to production. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog . If you’re reading this on the website and you’d like to receive the email version, you can subscribe here .

Like most organizations, Google Cloud is continually engaging with customers, partners, and policymakers to deliver technology capabilities that reflect their needs. When it comes to digital sovereignty solutions, Google Cloud has worked with customers for nearly a decade. Today, we’re pleased to announce significant technical and commercial updates on our sovereign cloud solutions for customers, and details on how we’re helping them achieve greater control, choice, and security in the cloud —..

Google Threat Intelligence Group’s (GTIG) mission is to protect Google’s billions of users and Google’s multitude of products and services. In late October 2024, GTIG discovered an exploited government website hosting malware being used to target multiple other government entities. The exploited site delivered a malware payload, which we have dubbed “TOUGHPROGRESS”, that took advantage of Google Calendar for command and control (C2).

Since November 2024, Mandiant Threat Defense has been investigating an UNC6032 campaign that weaponizes the interest around AI tools, in particular those tools which can be used to generate videos based on user prompts. UNC6032 utilizes fake “AI video generator” websites to distribute malware leading to the deployment of payloads such as Python-based infostealers and several backdoors.

GenAI is transforming the way people think about their work and giving businesses of all sizes an opportunity to re-imagine everything from their internal processes to the way they engage with their customers. These new capabilities are evolving at a rapid pace, and that creates new challenges for IT and security teams to protect their enterprise data. With so much work happening on the web, the browser offers a unique place to harness the value of AI across many different workflows.

Take this quiz about Google I/O 2025 to see how well you know what we announced this year at I/O.

New Gemini updates built directly into tools you use every day — like Gmail, Meet, Vids and Docs — are coming to Google Workspace.Gmail is getting personalized smart rep…

Project Starline is becoming Google Beam, a platform that uses AI to turn 2D video streams into realistic, immersive video calls.

The explosion of digital content from social media, smartphones, and other sources has created a massive amount of unstructured data like images, videos, and documents. To help you analyze this data, BigQuery is connected with Vertex AI, Google Cloud's powerful AI platform, so you can use advanced AI models, like Gemini 2.5 Pro/Flash, to understand the meaning hidden within your unstructured data.

Organizations depend on fast and accurate data-driven insights to make decisions, and SQL is at the core of how they access that data. With Gemini, Google can generate SQL directly from natural language — a.k.a. text-to-SQL. This capability increases developer and analysts’ productivity and empowers non-technical users to interact directly with the data they need.

Building cutting-edge AI models is exciting, whether you’re iterating in your notebook or orchestrating large clusters. However, scaling up training can present significant challenges, including navigating complex infrastructure, configuring software and dependencies across numerous instances, and pinpointing performance bottlenecks. At Google Cloud, we're focused on making AI training easier, whatever your scale.

In the event of a cloud incident, everyone wants swift and clear communication from the cloud provider, and to be able to leverage that information effectively. Personalized Service Health in the Google Cloud console addresses this need with fast, transparent, relevant, and actionable communications about Google Cloud service disruptions, customized to your specific footprint.

Data management is changing. Enterprises need flexible, open, and interoperable architectures that allow multiple engines to operate on a single copy of data. Apache Iceberg has emerged as the leading open table format, but in real-world deployments, customers often face a dilemma: embrace the openness of Apache Iceberg but compromise on fully managed, enterprise-grade storage management, or choose managed storage but sacrifice the flexibility of open formats.

The Google Data Cloud is a uniquely integrated platform built on Google’s planet-scale infrastructure, infused with AI, and features an open lakehouse architecture for multimodal data. Already, organizations like Snap Inc. credit Google's Data Cloud and open lakehouse architecture with empowering their data engineers and data scientists to do more with their data assets.

In today's data-driven world, understanding large datasets often requires numerous, complex non-additive 1 aggregation operations. But as the size of the data becomes massive 2 , these types of operations become computationally expensive and time-consuming using traditional methods. That's where Apache DataSketches come in. We're excited to announce the availability of Apache DataSketches functions within BigQuery, providing powerful tools for approximate analytics at scale.

If you’re building a generative AI application or an AI agent, there’s a high likelihood you’ll need to perform simultaneous searches on structured and unstructured data. For example, the prompt “Show me all pictures of sunsets I took in the past month” includes a structured part (the date is within the past month) and an unstructured part (the picture contains a sunset).

Google Cloud is pleased to announce the general availability of committed use discounts (‘CUDs’) for Red Hat Enterprise Linux. If you run consistent and predictable workloads on Compute Engine, you can utilize CUDs to save on Red Hat Enterprise Linux subscription costs by as much as 20% 1 compared to on-demand (or “PAYG”) prices. “Red Hat Enterprise Linux on Google Cloud provides a consistent foundation for hybrid cloud environments and a reliable, high-performance operating environment for...

Today, we’re excited to announce the general availability (GA) of GKE Data Cache , a powerful new solution for Google Kubernetes Engine to accelerate the performance of read-heavy stateful or stateless applications that rely on persistent storage via network attached disks. By intelligently utilizing high-speed local SSDs as a cache layer for persistent disks, GKE Data Cache helps you achieve lower read latency and higher queries per second (QPS), without complex manual configuration.

SAP and Google Cloud are deepening their collaboration across data analytics, AI, security, and more to deliver what customers need most: faster paths to business value, lower risk on complex projects, and smart, enterprise-ready innovation that’s built to serve real workloads — not hypothetical use cases. At SAP Sapphire 2025, we are excited to highlight multiple updates that will simplify how customers access SAP data with BigQuery, power new AI agents and capabilities with Gemini and...

We investigate and improve algorithms for fine-tuning large models with user-level differential privacy...

We celebrate Google Research highlights from I/O 2025, including our latest research breakthroughs and our contributions to Google’s Gemini models and generative AI products.

At our annual developer conference, we announced how we’re making AI even more helpful with Gemini.

Many events are taking place in this period! Last week I was at the AI Week in Italy. This week I’ll be in Zurich for the AWS Community Day – Switzerland . On May 22, you can join us remotely for AWS Cloud Infrastructure Day to learn about cutting-edge advances across compute, AI/ML, storage, networking, serverless technologies, and global infrastructure. Look for events near you for an opportunity to share your knowledge and learn from others.

Improving response quality for user queries is essential for AI-driven applications, especially those focusing on user satisfaction. For example, an HR chat-based assistant should strictly follow company policies and respond using a certain tone. A deviation from that can be corrected by feedback from users. This post demonstrates how Amazon Bedrock , combined with a user feedback dataset and few-shot prompting, can refine responses for higher user satisfaction.

Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Both models are now generally available in Amazon Bedrock , giving developers immediate access to both the model’s advanced reasoning and agentic capabilities.

Foundation models (FMs) have revolutionised AI capabilities, but adopting them for specific business needs can be challenging. Organizations often struggle with balancing model performance, cost-efficiency, and the need for domain-specific knowledge. This blog post explores three powerful techniques for tailoring FMs to your unique requirements: Retrieval Augmented Generation (RAG), fine-tuning, and a hybrid approach combining both methods.

Organizations today deal with vast amounts of unstructured data in various formats including documents, images, audio files, and video files. Often these documents are quite large, creating significant challenges such as slower processing times and increased storage costs. Extracting meaningful insights from these diverse formats in the past required complex processing pipelines and significant development effort.

We are excited to announce the availability of Gemma 3 27B Instruct models through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, developers and data scientists can now deploy Gemma 3, a 27-billion-parameter language model, along with its specialized instruction-following versions, to help accelerate building, experimentation, and scalable deployment of generative AI solutions on AWS.

It takes biopharma companies over 10 years , at a cost of over $2 billion and with a failure rate of over 90% , to deliver a new drug to patients. The Market to Molecule (M2M) value stream process, which biopharma companies must apply to bring new drugs to patients, is resource-intensive, lengthy, and highly risky. Nine out of ten biopharma companies are AWS customers, and helping them streamline and transform the M2M processes can help deliver drugs to patients faster, reduce risk, and bring...

Agentic Retrieval Augmented Generation (RAG) applications represent an advanced approach in AI that integrates foundation models (FMs) with external knowledge retrieval and autonomous agent capabilities. These systems dynamically access and process information, break down complex tasks, use external tools, apply reasoning, and adapt to various contexts. They go beyond simple question answering by performing multi-step processes, making decisions, and generating complex outputs.

Organizations across a wide range of industries are struggling to process massive amounts of unstructured video and audio content to support their core business applications and organizational priorities. Amazon Bedrock Data Automation helps them meet this challenge by streamlining application development and automating workflows that use content from documents, images, audio, and video. Recently, we announced two new capabilities that you can use to get custom insights from video and audio.

In the landscape of generative AI , organizations are increasingly adopting a structured approach to deploy their AI applications, mirroring traditional software development practices. This approach typically involves separate development and production environments, each with its own AWS account, to create logical separation, enhance security, and streamline workflows.

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 be ingesting customer comments and want to identify the language they are in. Traditionally, this requires an external process that complicates data ingest pipelines and can cause a pipeline to fail.

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 as Retrieval Augmented Generation (RAG) and workflows involving agents .

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 processing, and content creation at scale.

In the financial services industry, analysts need to switch between structured data (such as time-series pricing information), unstructured text (such as SEC filings and analyst reports), and audio/visual content (earnings calls and presentations). Each format requires different analytical approaches and specialized tools, creating workflow inefficiencies. Add on top of this the intense time pressure resulting from rapidly evolving industry conditions and narrow decision windows.

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 AWS customers using Terraform to define and manage their infrastructure-as-code (IaC), the current best practice for enabling Amazon SageMaker Projects carries a dependency.

Join us in Philadelphia on June 16–18, 2025, for AWS re:Inforce, where you can enhance your skills and confidence in cloud security, compliance, identity, and privacy. As an attendee, you will have access to hundreds of technical and non-technical sessions, an Expo featuring Amazon Web Services (AWS) experts and AWS Security Competency Partners , and keynote..

Customers often tell me that they want a simpler path to meet the compliance and industry regulatory mandates they have in their geographic regions. In our deep engagements with partners and customers, we have learned that one of the greatest challenges for customers is the translation of security and compliance requirements into distinct technical controls.

AWS Secrets Manager is a service that you can use to manage, retrieve, and rotate database credentials, application credentials, API keys, and other secrets throughout their lifecycles. You can use Secrets Manager to replace hard-coded credentials in application source code with a runtime call to the Secrets Manager service to retrieve credentials dynamically when you need them.

From proof of concepts to large scale production deployments, the rapid advancement of generative AI has ushered in unique opportunities for innovation, but it also introduces a new set of security challenges (and opportunities) that organizations must address.

We’re counting down to AWS re:Inforce , our annual cloud security event! We are thrilled to invite security enthusiasts and builders to join us in Philadelphia, PA June 16–18, 2025, for an immersive three-day journey into cloud security learning.

Triaging and quickly responding to security events is important to minimize impact within an AWS environment. Acting in a standardized manner is equally important when it comes to capturing forensic evidence and quarantining resources. By implementing automated solutions, you can respond to security events quickly and in a repeatable manner. Before implementing automated security solutions, it’s important for your security team to have a defined process and understanding of which actions to...

Traditional application deployment methods have become increasingly unsustainable. Management and delivery of applications to the end-users in an enterprise environment is a daunting task. With multiple versions of applications running across different systems and environments, IT teams often face time-consuming processes, increases administrative overhead, and high operational costs.

AWS Amplify Hosting has built web applications using a fixed instance environment. As applications grow more complex and require intensive build processes for dependency management, asset optimization, and comprehensive testing, developers need more powerful build environments to maintain productivity and deployment speed. Today, we’re excited to introduce customizable build instances for Amplify Hosting.

Introduction Email communication remains a critical component of business operations and customer engagement. As the digital landscape evolves, major mailbox providers continually update their policies to enhance security and user experience. This blog will explore the changes implemented by Microsoft for bulk senders trying to reach Outlook.com (supporting Hotmail.com, live.com consumer domain addresses). This follows the Google & Yahoo! bulk sender requirements changes in February of 2024.

Cross-origin resource sharing (CORS) is a security feature implemented by web browsers that controls which web pages or web applications are allowed to make requests to a different domain or origin . In other words, CORS is a mechanism that prevents a web page hosted on one domain from making requests for resources from a different domain unless the target domain explicitly allows it.

This post was jointly authored by Sara Alligood, Global Head of SAP at AWS, and Kai Mühlbauer, Head of AI Product & Partner Management at SAP Today at SAP Sapphire, Amazon Web Services, Inc. and SAP announced the launch of a new AI Co-Innovation Program to help partners build generative artificial intelligence (AI) applications and agents that help customers rapidly solve real-time business challenges.

One reason that customers choose Amazon DynamoDB is because it provides single-digit millisecond performance on any scale. However, this performance depends on an effective data model that supports the application’s access patterns and concurrency requirements. Multi-tenant applications (such as Software-as-a-Service or SaaS applications) introduce additional complexity with unique considerations around performance, scalability, and isolation.

In today’s digital enterprise landscape, organizations increasingly rely on asset management solutions to streamline their operations. Companies often find themselves managing the same physical assets across multiple IT and operational technology (OT) systems. One of the services that help IT teams track and manage enterprise IT assets is ServiceNow .

Managing storage resources efficiently in cloud environments is a challenge for organizations of all sizes. As businesses scale their operations, they often accumulate unused storage volumes that continue to generate costs without providing value. This ‘orphaned’ storage problem is particularly acute in containerized environments, where the complexity of storage lifecycle management can lead to oversight and inefficiency.

We believe the most successful organizations will be those that view AI not just as a tool for automation, but as a catalyst for transforming how they approach software development entirely. The real strategic advantage will come from reimagining software development processes and culture to fully leverage AI’s capabilities. This includes rethinking traditional metrics, redefining developer productivity, and creating space and cultural change for teams to experiment with new ways of working.

This post was co-authored by Elamaran Shanmugam, Sr. Specialist Partner Solutions Architect – Containers, Mikhail Shapirov, Principal Partner Solutions Architect – Industry Solutions, Jayaprakash Alawala, Principal Specialist Solutions Architect – Containers, Bhavye Sharma, Partner Solutions Architect Containers have revolutionized software delivery by providing a portable and consistent environment that addresses challenges related to complex frameworks and dependencies.

Modern application development demands faster, more efficient ways to build and deploy software. Over the past decade, serverless computing has emerged as a transformative approach to software development, enabling developers to focus on building applications without having to manage the underlying infrastructure. As developers build their applications using AWS Serverless Compute , they seek guidance on selecting appropriate services, understanding best practices, and implementation patterns...

In our previous post External Connectivity to Amazon VPC Lattice , we discussed how your applications could consume VPC Lattice services from clients external to Amazon Web Services (AWS), as well as cross- Region patterns. At re:Invent 2024, AWS made three significant announcements in the application networking space: releasing the VPC resources capability for AWS PrivateLink , support for accessing these resources through Amazon VPC Lattice , and the capability to create endpoints for VPC...

This blog post was jointly authored by Vara Bonthu, Principal OSS Specialist Solutions Architect and Omri Shiv, Senior Open Source ML Engineer Introduction We’re excited to announce the launch of AI on EKS: a new open source initiative from Amazon Web Services (AWS) designed to help customers deploy, scale, and optimize artificial intelligence/machine learning (AI/ML) workloads on Amazon Elastic Kubernetes Service (Amazon EKS) .

When it comes to running enterprise workloads, Microsoft SQL Server sits at the heart of many of these solutions providing the data needed to drive important business decisions. However, what is often not considered is the storage service underpinning the delivery of that critical data, and with the right tools and management applied, how that storage service can bring even more agility and flexibility to Microsoft SQL server deployments.

As companies expand globally, they must be able to architect highly available and fault-tolerant systems across multiple AWS Regions. With such scale, a company can find itself in this position when designing a caching solution across its multi-Region infrastructure. In this post, we dive deep into how to use Amazon ElastiCache for Valkey , a fully managed in-memory data store with Redis OSS and Valkey compatibility, and the Amazon ElastiCache for Valkey Global Datastore feature set.

This post is co-written with Gurvinder Arora and Utkarsh Jain from Impetus Technologies. Introduction LeapLogic is a cloud transformation accelerator designed to simplify, automate, and expedite legacy migration to modern cloud ecosystems. It translates workloads from traditional data warehouses, ETL (Extract, Transform, and Load), BI (Business Intelligence) reports, Hadoop, scheduled batch jobs and other environments into optimized, cloud-native formats, minimizing manual efforts and risks.

High-performance applications with unlimited throughput capabilities pose significant monitoring challenges, especially when tracking real-time metrics, utilization, and throttling events across distributed database workloads. Near real-time visibility into metrics is crucial for application performance and cost optimization. AWS allows you to seamlessly integrate multiple services to tackle these operational complexities.

Amazon Q Developer just launched a new agentic experience within the AWS Management Console , that enables builders to get deeper insights about their AWS resources and improve their operational troubleshooting efficiency. This expands the agentic capabilities of Amazon Q Developer from both the integrated development environment (IDE) and command line interface (CLI) to the AWS console.

Today I am happy to announce we are releasing Strands Agents . Strands Agents is an open source SDK that takes a model-driven approach to building and running AI agents in just a few lines of code. Strands scales from simple to complex agent use cases, and from local development to deployment in production. Multiple teams at AWS already use Strands for their AI agents in production, including Amazon Q Developer, AWS Glue, and VPC Reachability Analyzer.

This post was co-written with JB Brown from Smartsheet. Organizations are often seeking ways to enhance developer productivity while maintaining cost-efficiency. AI-powered coding assistants are effective solutions to accelerate development cycles, but implementing these solutions at scale while managing costs presents unique challenges. Roo Code , an AI-powered autonomous coding agent located directly in developers’ editors, represents the latest evolution in AI-assisted development.

At AWS, open standards run deep in our DNA, driving all that we do. That’s why we decided to build Amazon Elastic Cloud Compute (EC2) as a protocol-agnostic cloud computing service and Amazon SageMaker as a framework-agnostic deep learning service. Our commitment to openness continues as we enter the agentic AI era, extending to inter-agent communication.

The automotive industry is undergoing a transformative shift as vehicles evolve from purely mechanical systems into sophisticated computing platforms. At the heart of this evolution are software-defined vehicles (SDVs), where software capabilities increasingly drive innovation and differentiation in the industry. Modern vehicles now contain up to 100 million lines of code across multiple electronic control units (ECUs), managing everything from safety systems to driver assistance and...

Every year in the United States, distracted driving claims thousands of lives and causes immense financial damage. More than 1.6 million accidents annually are caused by cell phone use while driving , and another 1.5 million result from drowsy drivers falling asleep at the wheel. These devastating—and preventable—accidents have sparked a major push for enhanced driver safety.

In this post, you will learn how Amazon Web Services (AWS) customer, Maya , the Philippines’ leading fintech company and digital bank, built an API management platform to address the growing complexities of managing multiple APIs hosted on Amazon API Gateway . API Gateway is a fully managed service that you can use to create RESTful and WebSocket APIs. At Maya, different teams build APIs to expose their services to merchants.

More than 1,000 life sciences leaders from over 400 organizations joined Amazon Web Services (AWS) in New York City on May 6, 2025 for the seventh Annual AWS Life Sciences Symposium . They were united by a bold theme: “Building for Breakthroughs – AI-powered Innovations Transforming the Pharmaceutical Value Chain.” Across 26 sessions, featuring insights by 39 industry pioneers, the event spotlighted how advanced AI is driving step-change innovation, accelerating the development of life-saving...

Introduction Mastercard’s AI-powered real-time fraud prevention platform, Brighterion, has been delivering advanced fraud detection and risk management solutions to enterprise customers for over 20 years. Trusted by more than 2,000 clients worldwide, including 74 of the top 100 U.S. banks, Mastercard’s Brighterion AI platform scores over 150 billion transactions annually, helping organizations prevent fraud while optimizing approval rates.

This post was coauthored with engineers at Cash App, including Ben Apprederisse (Platform Engineering Technical Lead), Jan Zantinge (Traffic Engineer), and Rachel Sheikh (Compute Engineer). Cash App , a leading peer-to-peer payments and digital wallet service from Block, Inc., has grown to serve over 57 million users since its launch in 2013. Providing diverse offerings like instant money transfers, debit cards, stock and bitcoin investments, personal loans, and tax filing, Cash App...

Healthcare organizations are constantly seeking innovative ways to enhance patient experiences, boost developer productivity, and streamline processes. The advent of Generative AI (Gen AI) has opened up a world of possibilities, allowing healthcare providers to harness the power of advanced language models and unlock new realms of efficiency and innovation.

Opportunities for telecoms with small language models (SLMs) explored how telecom operators can use SLMs deployed on Customer Premises Equipment (CPE) and Internet of Things (IoT) devices to enable autonomous environments and handle routine information requests. Subsequently, in Distributed inference with collaborative AI agents for telco-powered smart-X , we further explored the spectrum of distributed computing locations available to telecoms, emphasizing the need to push inference to edge...

On May 6, more than 1,000 life sciences leaders from over 400 organizations convened in New York City for the seventh Annual AWS Life Sciences Symposium . United under the theme, “Building for Breakthroughs – AI-powered Innovations Transforming the Pharmaceutical Value Chain,” the event showcased how generative AI has moved beyond its hype to become a practical catalyst for pharmaceutical transformation.

Generated by Amazon Nova in Amazon Bedrock Successful generative AI (gen AI) implementation hinges on a critical and fundamental decision that many organizations overlook: selecting the right foundation models (FMs) for their unique business challenges. Every organization has diverse needs requiring specific AI capabilities—what works perfectly for one use case may be imperfect or impractical for another due to cost-benefit tradeoffs, operational inefficiencies, or misalignment with business...

Large organizations increasingly adopt software as a service (SaaS) solutions to focus on business priorities, reduce infrastructure management overhead, and optimize costs. These organizations expect SaaS vendors to provide customizability facilities for tailoring the solution behavior according to their needs. Although traditional approaches like feature flags and webhooks offer some flexibility, they often fall short of providing a high degree of customizability.

Companies of every size have realized benefits from moving to the AWS Cloud, including a lower cost of operations, stronger operational resilience, and a smaller carbon footprint. As part of that transition, cloud operations (CloudOps) plays a crucial role in keeping cloud infrastructure running smoothly. For aspiring cloud architects and system administrators transitioning to Amazon Web Services (AWS) , building a strong foundation in CloudOps is the first step towards effectively managing and.