Key Insights From AWS

One of the joys of being in the middle of tech disruptions over the past few decades is that you begin to see common patterns and start building an intuition for what could be a pivotal moment. This week might be one of those.   AWS made a landmark move in enterprise AI by launching the AI Agents & Tools Marketplace—rolling out more than 900 agents from leading partners, all easily deployable through their Bedrock AgentCore platform. This means organizations can access, procure, and scale autonomous agents across countless business functions with real interoperability and governance at scale.   For those of you who were around during the pre-iPhone era, carrying a BlackBerry, Handspring, or Palm 680, you would recall how every device—from Palm Pilots to Nokias—required its own code, incompatible apps, and brought endless technical headaches. The breakthrough came when Apple unified the ecosystem, letting developers deploy once and reach everyone, igniting mobile innovation.   This is what today's enterprise AI landscape feels like to me and my team as we work with organizations across industries. We see a race to deploy AI agents using whatever frameworks or tools they are familiar with, integrating them haphazardly with the data sources they can access. Success is measured in pockets: one group can showcase a "deployed agent," but it's often a unique, bespoke build that's hard to scale and even harder to replicate across the business.   Leading vendors have addressed this by offering marketplaces. However, most have somewhat closed ecosystems, limited interoperability, lock-in to a single vendor or cloud, and use cases that haven't been powered through a strong partner network. In short, nothing that genuinely unified or democratized enterprise AI adoption.   This is why AWS's announcement represents an actual inflection point. They are: A. Opening the marketplace to any framework—LangChain, CrewAI, open source or commercial—avoiding lock-in. B. Enabling deployment wherever needed: serverless, on-prem, multi-cloud, or as APIs and containers. and C. Securing agent access and data with enterprise-grade governance and audit. It feels like AWS has unified the space, just as Apple once unified the mobile market. The implications for enterprise AI adoption are profound—we're moving from fragmented experimentation to scalable, production-ready ecosystems. Given our focus on AI-led transformations through our deep domain knowledge, Genpact was one of the professional services partners highlighted at the announcement, as we contribute to the marketplace with our agents. (Kudos to Sanjeev Vohra, Sreekanth Menon, Murat Aksu, and Nidhi Srivastava)   Is the age of scattered, isolated AI builds finally ending? Are we entering the era of unified, enterprise-scale AI?   #AI #AWS #AgentMarketplace #Genpact #DigitalTransformation #EnterpriseAI #Innovation

As we step into 2025, I'm filled with optimism about the transformative power of technology in business. Here are three game-changing developments that I believe will shape our digital landscape in the year ahead and beyond:   1️ Buy with AWS: Redefining Software Procurement Unveiled at re:Invent, the new "Buy with AWS" feature is set to transform how customers discover and purchase cloud software. By empowering partners to embed this functionality on their sites, we’re creating a seamless bridge between sellers and buyers. This innovation not only accelerates sales for sellers but also simplifies the software discovery and purchasing process for customers. With secure transactions using AWS accounts and centralized management within AWS Marketplace, it's a win-win for all parties involved.   2️ Product-led Growth: Databricks Quick Launch The SaaS Quick Launch for Databricks is a prime example of how we're removing barriers to adoption and generating new customers for our partners in a zero touch sales model. By automating the installation and configuration process, we've reduced a complex 132-step manual setup to just 6 steps, while cutting context switches from 18 to just 3. This frictionless onboarding experience for Databricks on AWS showcases how we're making advanced technologies more accessible than ever. And this is just the tip of the iceberg!   3️ Unprecedented Buyer Momentum It's thrilling to see that over 99% of our top 1,000 AWS customers are now leveraging AWS Marketplace. But it's crucial to understand that Marketplace isn't just a website - it's a digital storefront streamlining how customers find, buy, and implement solutions, how partners acquire customers, how we co-sell with partners, and how these partners integrate with AWS to provide a seamless experience.   As we look ahead, these developments signal a future where technology adoption is not just faster and smoother, but truly transformative. The barriers between innovation and implementation are crumbling, paving the way for unprecedented growth and efficiency across industries. Having been part of this incredible journey of growth and innovation at AWS for over 9 years, I'm more excited than ever about what lies ahead. The possibilities are limitless, and I can't wait to see how these advancements will shape our world.

“AWS’ AI business is a multibillion-dollar revenue run rate business that continues to grow at a triple-digit year-over-year percentage and is growing more than 3x faster at this stage of its evolution as AWS itself grew, and we felt like AWS grew pretty quickly.” “Our AI business is on track to surpass an annual revenue run rate of $10 billion next quarter, which will make it the fastest business in our history to reach this milestone.” Those quotes from Amazon & Microsoft last week underscore the dramatic transformation in cloud growth rates. Across the 3 major clouds, the growth rates have increased between 27% and 58% from their nadir about a year ago. But the businesses are 60% bigger today than they were the last time they touched those growth rates. Plus the operating margins of these companies is massive at around 40% for the top two. GCP’s is the lowest, but accelerating rapidly. It was 3.1% last year. Microsoft & others have said their growth is limited by GPUs which will continue until late next year. Amazon & Google are developing their own chips : “As customers approach higher scale in their implementations, they realize quickly that AI can get costly. It’s why we’ve invested in our own custom silicon in Trainium for training and Inferentia for inference. The second version of Trainium, Trainium2, is starting to ramp up in the next few weeks and will be very compelling for customers on price performance.” And internally, the impacts are real. Google said 25% of new code written is AI generated. AWS quantified it further : “The team has added all sorts of capabilities in the last few months, but the very practical use case recently shared where Q Transform saved Amazon’s teams $260 million and 4,500 developer years in migrating over 30,000 applications to new versions of the Java JDK.” All of these advances are expensive: “We expect to spend approximately $75 billion in CapEx in 2024. The majority of the spend is to support the growing need for technology infrastructure.” In total, these hyperscalers invested about $52b last quarter in data centers & GPUs. But the chips are now valuable for longer than they were (again from AWS). “We made the change in 2024 to extend the useful life of our servers. This added about 200 basis points of margin year-over-year.” The most important metric for these businesses will be profit dollars per GPU dollar cost. Which chip design will produce the best profits : Google’s TPUs, Amazon’s Inferentia/Tranium, or Microsoft’s Maia and Cobalt? It’s hard to calculate exactly this figure because the public data isn’t granular enough to compare across the three. But over time we should be able to infer major differences.

AI development comes with real challenges. Here's a practical overview of three ways AWS AI infrastructure solves common problems developers face when scaling AI projects: accelerating innovation, enhancing security, and optimizing performance. Let's break down the key tools for each: 1️⃣ Accelerate Development with Sustainable Capabilities: • Amazon SageMaker: Build, train, and deploy ML models at scale • Amazon EKS: Run distributed training on GPU-powered instances, deploy with Kubeflow • EC2 Instances:   - Trn1: High-performance, cost-effective for deep learning and generative AI training   - Inf1: Optimized for deep learning inference   - P5: Highest performance GPU-based instances for deep learning and HPC   - G5: High-performance for graphics-intensive ML inference • Capacity Blocks: Reserve GPU instances in EC2 UltraClusters for ML workloads • AWS Neuron: Optimize ML on AWS Trainium and AWS Inferentia 2️⃣ Enhance Security: • AWS Nitro System: Hardware-enhanced security and performance • Nitro Enclaves: Create additional isolation for highly sensitive data • KMS: Create, manage, and control cryptographic keys across your applications 3️⃣ Optimize Performance: • Networking:   - Elastic Fabric Adapter: Ultra-fast networking for distributed AI/ML workloads   - Direct Connect: Create private connections with advanced encryption options   - EC2 UltraClusters: Scale to thousands of GPUs or purpose-built ML accelerators • Storage:   - FSx for Lustre: High-throughput, low-latency file storage   - S3: Retrieve any amount of data with industry-leading scalability and performance   - S3 Express One Zone: High-performance storage ideal for ML inference Want to dive deeper into AI infrastructure? Check out 🔗 https://lnkd.in/erKgAv39 You'll find resources to help you choose the right cloud services for your AI/ML projects, plus opportunities to gain hands-on experience with Amazon SageMaker. What AI challenges are you tackling in your projects? Share your experiences in the comments! 📍 save + share! 👩🏻💻 follow me (Brooke Jamieson) for the latest AWS + AI tips 🏷️  Amazon Web Services (AWS), AWS AI, AWS Developers #AI #AWS #Infrastructure #CloudComputing #LIVideo

AWS Data Platform Reference Architecture! In today's data-driven world, organizations need a robust data platform to handle the growing volume, variety, and velocity(3 V’s) of data. A well-designed data platform provides a scalable, secure, and efficient infrastructure for data management, processing, and analysis. It transforms raw data into actionable insights that can inform strategic decision-making, drive innovation, and achieve business objectives. Let's delve into some key components of this architecture: ✅Centralized Data Repository: Amazon S3 acts as a centralized storage hub for both structured and unstructured data, ensuring durability, availability, and scalability. ✅Streamlined Data Transformation: AWS Glue simplifies the process of extracting, transforming, and loading (ETL) data into usable formats, preparing it for downstream analysis. ✅Powerful Data Analytics: Amazon Redshift, a fully managed data warehouse, supports complex SQL queries on large datasets, enabling organizations to gain deep insights from their data. ✅Efficient Big Data Processing: Amazon EMR, a cloud-native big data platform, handles massive data volumes using frameworks like Hadoop, Spark, and Hive. ✅Real-time Data Streaming: Amazon Kinesis enables real-time ingestion, buffering, and analysis of data streams from various sources, powering real-time applications and insights. ✅Event-driven Automation: AWS Lambda offers serverless computing, executing code in response to events, automating tasks and triggering other services. ✅Simplified Search and Analytics: Amazon Elasticsearch Service provides a managed search and analytics service, making it easy to analyze logs, perform text-based search, and enable real-time analytics. ✅Seamless Data Visualization and Sharing: Amazon Quicksight empowers users to explore and share data insights through interactive visualizations and reports. ✅Automated Data Workflow Orchestration: AWS Data Pipeline automates and orchestrates data-driven workflows across various AWS services, ensuring consistency and simplifying data management. ✅Machine Learning Made Easy: Amazon SageMaker simplifies the process of building, training, and deploying machine learning models for data analysis and predictions. ✅Centralized Metadata Management: The AWS Glue Data Catalog serves as a central repository for metadata, storing information about data sources, transformations, and schemas, facilitating data discovery and management. ✅Data Governance for Quality and Trust: Data governance ensures data quality, security, compliance, and privacy through policies, procedures, and controls, maintaining data integrity and compliance. Empowering a Data-driven Future A data platform architecture transforms data into valuable assets, enabling informed decisions and business growth. Source: AWS Tech blogs Follow - Chandresh Desai, Cloudairy #cloudcomputing #data #aws

10 things I learned at Amazon Web Services (AWS)'s re:Invent 2023 CEO Adam Selipsky and 50K+ attendees carried on the annual conference that started in 2012 under Andy Jassy "to keep changing the way some core computing standards work.” A catalyst of change in 2023 and going forward is clearly AI. AI is changing more than computing standards … it is forcing companies to rethink and transform their businesses. It is critical to learn about and influence the direction tech and their industries are moving. This gathering of though leaders is an opportunity to do so as we meet, share ideas, and get inspired by a vibrant community. Take aways:  1. The future is here, but is not evenly distributed. This quote from William Gibson highlights that the things that will constitute the 'normal' within the lives of those in the future, already exist for some today 2. Instead of committing heavily to one specific solution, we should be investing in the opportunity for choice 3. To manage AI expectations of senior leaders, we should help them understand what is easy and hard. This education should include day-to-day leaders and Boards of Directors 4. Invention is hard. Per Nikola Tesla “The progressive development of man is vitally dependent on invention. - This is the difficult task of the inventor who is often misunderstood and unrewarded.” 5. When looking to differentiate through innovation, speed is disproportionally important. The first mover will usually gain an advantage. You should look to “front load” things that are challenging that you don’t know how to do yet 6. To limit hallucinations, you have to constrain a model and customize it with your data 7. To customize models, firms are using a combination of 1) Fine Tuning 2) Retrieval Augmented Generation (RAG) and 3) Continued Pre-Training 8. AWS is differentiating itself with 3x the data centers versus the next CSP, 60% more services, and announcements on infrastructure, chipsets, foundation models, partnerships, security, controls, policy management, and more  9. Amazon Q makes it easy to tailor GenAI to a business by securely leveraging data through simple prompt and low code interfaces and RAG 10. Although intuitive on the surface, GenAI apps are built upon 1) a foundation of infrastructure for model training and inference, 2) a large assortment of FMs with tools to build effectively, and 3) a comprehensive set of services for storing, querying, and analyzing data. Given the week’s focus on AI, many of these are ‘borrowed’ from Swami Sivasubramanian; VP, Database, Analytics and ML and Matt Wood; VP, Technology and GenAI lead for AWS. I’m thankful that we also have Matt as Guardian Life’s Executive Sponsor and partner in our own specific journey of transformation. Thanks to Andrew J. McMahon, Dean A. Del Vecchio, and Guardian’s senior management for their commitment to our transformation journey. Every Transformation Needs a Guardian! #awsreinvent2023 #reinvent2023 #aws  #guardianlife