by Justin Cook
Hello!
So many exciting announcements this year including Nova, Q Developer, Graviton4, and more, so I have combined and refined all sources for the announcements in order.
Matt Garman, AWS CEO
Amazon EC2 Trn2 Instances and Trn2 UltraServers: Amazon EC2 Trn2 instances, powered by 16 AWS Trainium2 chips, are purpose-built for generative AI and are the most powerful EC2 instances for training and deploying models with hundreds of billions to trillion+ parameters. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-ec2-trn2-instances-available/https://aws.amazon.com/blogs/aws/amazon-ec2-trn2-instances-and-trn2-ultraservers-for-aiml-training-and-inference-is-now-available/
Amazon S3 Tables: Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables are specifically optimized for analytics workloads, resulting in up to 3x faster query throughput and up to 10x higher transactions per second compared to self-managed tables. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-s3-tables-apache-iceberg-tables-analytics-workloads/https://aws.amazon.com/blogs/aws/new-amazon-s3-tables-storage-optimized-for-analytics-workloads/
Amazon S3 Metadata: Amazon S3 Metadata is the easiest and fastest way to help you instantly discover and understand your S3 data with automated, easily-queried metadata that updates in near real-time. This helps you to curate, identify, and use your S3 data for business analytics, real-time inference applications, and more. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-s3-metadata-preview/https://docs.aws.amazon.com/AmazonS3/latest/userguide/metadata-tables-overview.html
Amazon Aurora DSQL: Aurora DSQL allows you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resiliency effortless for your applications and offers the fastest distributed SQL reads and writes. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-aurora-dsql-preview/https://aws.amazon.com/blogs/database/introducing-amazon-aurora-dsql/
Amazon DynamoDB global tables multi-region strong consistency: DynamoDB global tables is a fully managed, serverless, multi-Region, and multi-active database used by tens of thousands of customers. With this new capability, you can now build highly available multi-Region applications with a Recovery Point Objective (RPO) of zero, achieving the highest level of resilience. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-dynamodb-global-tables-previews-multi-region-strong-consistency/https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GlobalTables.html
Amazon Nova: Amazon Nova, a new generation of state-of-the-art (SOTA) foundation models (FMs) that deliver frontier intelligence and industry leading price performance. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-nova-foundation-models-bedrock/https://aws.amazon.com/blogs/aws/introducing-amazon-nova-frontier-intelligence-and-industry-leading-price-performance/
Amazon Bedrock Model Distillation: Amazon Bedrock Model Distillation automates the process needed to generate synthetic data from the teacher model, trains and evaluates the student model, and then hosts the final distilled model for inference. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-bedrock-model-distillation-preview/https://aws.amazon.com/blogs/aws/build-faster-more-cost-efficient-highly-accurate-models-with-amazon-bedrock-model-distillation-preview/
Amazon Bedrock Guardrails now supports Automated Reasoning checks: With the launch of the Automated Reasoning checks safeguard in Amazon Bedrock Guardrails, AWS has become the first and only major cloud provider to integrate automated reasoning in our generative AI offerings. Automated Reasoning checks help detect hallucinations and provide a verifiable proof that a large language model (LLM) response is accurate. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-bedrock-guardrails-automated-reasoning-checks-preview/https://aws.amazon.com/blogs/aws/prevent-factual-errors-from-llm-hallucinations-with-mathematically-sound-automated-reasoning-checks-preview/
Amazon Bedrock multi-agent collaboration: Amazon Bedrock now supports multi-agent collaboration, allowing organizations to build and manage multiple AI agents that work together to solve complex workflows. This feature allows developers to create agents with specialized roles tailored for specific business needs, such as financial data collection, research, and decision-making. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-bedrock-multi-agent-collaboration/https://aws.amazon.com/blogs/aws/introducing-multi-agent-collaboration-capability-for-amazon-bedrock/
Amazon Q Developer: Q Developer is a generative AI-powered assistant for designing, building, testing, deploying, and maintaining software. Its agents for software development have a deep understanding of your entire code repos, so they can accelerate many tasks beyond coding. With this new capability, Q Developer can help you understand your existing code bases faster, or quickly document new features, so you can focus on shipping features for your customers. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-generate-documentation-source-code/https://aws.amazon.com/blogs/aws/new-amazon-q-developer-agent-capabilities-include-generating-documentation-code-reviews-and-unit-tests/
GitLab Duo with Amazon Q: AWS announces a preview of GitLab Duo with Amazon Q, embedding advanced agent capabilities for software development and workload transformation directly in GitLab's enterprise DevSecOps platform. Using GitLab Duo, developers can delegate issues to Amazon Q agents using quick actions. to build new features faster, maximize quality and security with AI-assisted code reviews, create and execute unit tests, and upgrade a legacy Java codebase. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/gitlab-duo-amazon-q-preview/https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-automatic-unit-test-generation/https://aws.amazon.com/blogs/aws/new-amazon-q-developer-agent-capabilities-include-generating-documentation-code-reviews-and-unit-tests/
Amazon Q Developer now provides transformation capabilities for .NET porting: AWS announces new generative-AI powered transformation capabilities of Amazon Q Developer in public preview to accelerate porting of .NET Framework applications to cross-platform .NET. Using these capabilities, you can modernize your Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-transformation-net-porting-preview/https://aws.amazon.com/blogs/aws/announcing-amazon-q-developer-transformation-capabilities-for-net-preview/
Amazon Q Developer transformation capabilities for VMware: AWS announces the preview of Amazon Q Developer transformation capabilities for VMware, the first generative AI–powered assistant that can simplify and accelerate the migration and modernization of VMware workloads to Amazon Elastic Compute Cloud (EC2). Check out:https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-transformation-capabilities-vmware-preview/https://aws.amazon.com/blogs/migration-and-modernization/amazon-q-developer-agents-for-transformation-of-vmware-workloads/https://aws.amazon.com/blogs/migration-and-modernization/getting-started-with-amazon-q-developer-transform-for-vmware/
Amazon Q Developer transformation capabilities for mainframe modernization: AWS announces new generative AI–powered capabilities of Amazon Q Developer in public preview to help customers and partners accelerate large-scale assessment and modernization of mainframe applications. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-transformation-mainframe-modernization/https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/transform.html
Amazon Q Developer adds operational investigation capability: Amazon Q Developer now helps you accelerate operational investigations across your AWS environment in just a fraction of the time. With a deep understanding of your AWS cloud environment and resources, Amazon Q Developer looks for anomalies in your environment, surfaces related signals for you to explore, identifies potential root-cause hypothesis, and suggests next steps to help you remediate issues faster. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-developer-operational-investigation-preview/https://aws.amazon.com/blogs/aws/investigate-and-remediate-operational-issues-with-amazon-q-developer/
Amazon Q Business introduces over 50 actions for popular business applications and platforms: This enhancement allows Amazon Q Business users to complete tasks in other applications without leaving the Amazon Q Business interface, improving the user experience and operational efficiency. The new plugins cover a wide range of widely used business tools, including PagerDuty, Salesforce, Jira, Smartsheet, and ServiceNow. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-q-business-50-actions-business-applications-platforms/https://aws.amazon.com/blogs/aws/amazon-q-business-is-adding-new-workflow-automation-capability-and-50-action-integrations/
Amazon Q in QuickSight unifies insights from structured and unstructured data: Now generally available, Amazon Q in QuickSight provides users with unified insights from structured and unstructured data sources through integration with Amazon Q Business. With Amazon Q in QuickSight business users can now augment insights from traditional BI data sources such as databases, data lakes and data warehouses, with contextual information from unstructured sources. Check out: https://aws.amazon.com/blogs/business-intelligence/integrate-unstructured-data-into-amazon-quicksight-using-amazon-q-business/https://aws.amazon.com/blogs/machine-learning/query-structured-data-from-amazon-q-business-using-amazon-quicksight-integration/
Introducing the next generation of Amazon SageMaker: AWS announces the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. This launch brings together widely adopted AWS machine learning and analytics capabilities and provides an integrated experience for analytics and AI with unified access to data and built-in governance. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/next-generation-amazon-sagemaker/https://aws.amazon.com/blogs/aws/introducing-the-next-generation-of-amazon-sagemaker-the-center-for-all-your-data-analytics-and-ai/
Amazon SageMaker Lakehouse: AWS announces Amazon SageMaker Lakehouse, a unified, open, and secure data Lakehouse that simplifies your analytics and artificial intelligence (AI). Amazon SageMaker Lakehouse unifies all your data across Amazon S3 data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/aws-announces-amazon-sagemaker-lakehouse/https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-sagemaker-lakehouse-redshift-zero-etl-integrations-eight-applications/https://aws.amazon.com/blogs/aws/simplify-analytics-and-aiml-with-new-amazon-sagemaker-lakehouse/
Amazon SageMaker Unified Studio: AWS announces the next generation of Amazon SageMaker, including the preview launch of Amazon SageMaker Unified Studio, an integrated data and AI development environment that enables collaboration and helps teams build data products faster. SageMaker Unified Studio brings together familiar tools from AWS analytics and AI/ML services for data processing, SQL analytics, machine learning model development, and generative AI application development. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/preview-amazon-sagemaker-unified-studio/
The Announcements from DeSantis!
AWS Graviton: Graviton4, newly announced this week, is capable of producing 40% more performance (than Graviton3) in real workload using NGINX. Graviton processors are powering many of the popular AWS services:
AWS Nitro System: All new AWS compute services in the past couple of years are powered by the Nitro System, which offers better performance and hardware-enforced separation.
AWS Trainium: Peter Desantis shared information about the AWS Trainium processors for generative AI workloads, and its architecture.
Systolic Array: A systolic array is a specialized architecture used in parallel processing, particularly effective for tasks like matrix multiplication and convolution operations in deep learning.
Neuron Kernel Interface (NKI): The Neuron Kernel Interface (NKI) is a programming interface introduced by AWS as part of the Neuron SDK, designed to optimize compute kernels specifically for AWS Trainium and Inferentia chips. It enables developers to create high-performance kernels that enhance the capabilities of deep learning models.
Announcement - Latency-optimized inference option for Amazon Bedrock:
Latency-optimized inference for foundation models in Amazon Bedrock is now available in public preview, delivering faster response times and improved responsiveness for AI applications. Currently, these new inference options support Anthropic's Claude 3.5 Haiku model and Meta's Llama 3.1 405B and 70B models offering reduced latency compared to standard models without compromising accuracy.
UltraCluster 2.0 and the 10p10u network: The last information discussed in the keynote was the UltraCluster and its underlying network which AWS internally calls 10p10u.
Amazon SageMaker HyperPod flexible training plans: Amazon SageMaker HyperPod announces flexible training plans, a new capability that allows you to train generative AI models within your timelines and budgets. Gain predictable model training timelines and run training workloads within your budget requirements, while continuing to benefit from features of SageMaker HyperPod such as resiliency, performance-optimized distributed training, and enhanced observability and monitoring. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-sagemaker-hyperpod-flexible-training-plans/https://aws.amazon.com/blogs/aws/meet-your-training-timelines-and-budgets-with-new-amazon-sagemaker-hyperpod-flexible-training-plans/https://docs.aws.amazon.com/sagemaker/latest/dg/reserve-capacity-with-training-plans.html
Amazon SageMaker HyperPod task governance: Amazon SageMaker HyperPod now provides you with centralized governance across all generative AI development tasks, such as training and inference. You have full visibility and control over compute resource allocation, ensuring the most critical tasks are prioritized and maximizing compute resource utilization, reducing model development costs by up to 40%.Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/task-governance-amazon-sagemaker-hyperpod/https://aws.amazon.com/blogs/aws/maximize-accelerator-utilization-for-model-development-with-new-amazon-sagemaker-hyperpod-task-governance/
AI apps from AWS partners now available in Amazon SageMaker: Amazon SageMaker partner AI apps, a new capability that enables customers to easily discover, deploy, and use best-in-class machine learning (ML) and generative AI (GenAI) development applications from leading app providers privately and securely, all without leaving Amazon SageMaker AI so they can develop performant AI models faster. Check out: https://aws.amazon.com/about-aws/whats-new/2024/12/amazon-sagemaker-partner-ai-apps/https://docs.aws.amazon.com/sagemaker/latest/dg/partner-apps.html
Poolside in Amazon Bedrock: Poolside's generative AI Assistant puts the power of poolside's malibu and point models right inside of your developers' Integrated Development Environment (IDE). poolside models are fine-tuned on your team's interactions, code base, practices, libraries, and knowledge bases.
Stability AI in Amazon Bedrock Three of Stability AI's newest cutting-edge text-to-image models are now available in Amazon Bedrock, providing high-speed, scalable, AI-powered visual content creation capabilities.
Luma AI in Amazon Bedrock:Luma AI is a startup building multimodal foundation models (FMs) and software products with the goal of helping consumers and professionals across industries creatively grow, ideate, and build. Luma AI’s goal is to create an “imagination engine” where users can play with and build new worlds without limiting the imagination. Check out: https://aws.amazon.com/bedrock/luma-ai/
Amazon Bedrock Marketplace: Amazon Bedrock Marketplace provides generative AI developers access to over 100 publicly available and proprietary foundation models (FMs), in addition to Amazon Bedrock’s industry-leading, serverless models. Customers deploy these models onto SageMaker endpoints where they can select their desired number of instances and instance types. Amazon Bedrock Marketplace models can be accessed through Bedrock’s unified APIs, and models which are compatible with Bedrock’s Converse APIs can be used with Amazon Bedrock’s tools such as Agents, Knowledge Bases, and Guardrails.
Amazon Bedrock supports prompt caching: Prompt caching is a new capability that can reduce costs by up to 90% and latency by up to 85% for supported models by caching frequently used prompts across multiple API calls. It allows you to cache repetitive inputs and avoid reprocessing context, such as long system prompts and common examples that help guide the model’s response. When cache is used, fewer computing resources are needed to generate output. As a result, not only can we process your request faster, but we can also pass along the cost savings from using fewer resources.
Amazon Bedrock Intelligent Prompt Routing: Amazon Bedrock Intelligent Prompt Routing routes prompts to different foundational models within a model family, helping you optimize for quality of responses and cost. Using advanced prompt matching and model understanding techniques, Intelligent Prompt Routing predicts the performance of each model for each request and dynamically routes each request to the model that it predicts is most likely to give the desired response at the lowest cost. Customers can choose from two prompt routers in preview that route requests either between Claude Sonnet 3.5 and Claude Haiku, or between Llama 3.1 8B and Llama 3.1 70B.
Amazon Kendra Generative AI Index: Amazon Kendra is an AI-powered search service enabling organizations to build intelligent search experiences and retrieval augmented generation (RAG) systems to power generative AI applications. Starting today, AWS customers can use a new index - the GenAI Index for RAG and intelligent search. With the Kendra GenAI Index, customers get high out-of-the-box search accuracy powered by the latest information retrieval technologies and semantic models.
Amazon Bedrock Knowledge Bases supports structured data retrieval: Amazon Bedrock Knowledge Bases now supports natural language querying to retrieve structured data from your data sources. With this launch, Bedrock Knowledge Bases offers an end-to-end managed workflow for customers to build custom generative AI applications that can access and incorporate contextual information from a variety of structured and unstructured data sources. Using advanced natural language processing, Bedrock Knowledge Bases can transform natural language queries into SQL queries, allowing users to retrieve data directly from the source without the need to move or preprocess the data.
Amazon Bedrock Knowledge Bases now supports GraphRAG: GraphRAG enhances generative AI applications by providing more accurate and comprehensive responses to end users by using RAG techniques combined with graphs. Check out: https://aws.amazon.com/blogs/aws/new-amazon-bedrock-capabilities-enhance-data-processing-and-retrieval/
Amazon Bedrock Data Automation: Amazon Bedrock Data Automation (BDA), a new feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. These insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. Developers can also customize BDA’s output to generate specific insights in consistent formats required by their systems and applications.
Amazon Bedrock Guardrails Multimodel toxicity detection: Amazon Bedrock Guardrails now supports multimodal toxicity detection for image content, enabling organizations to apply content filters to images. This new capability with Guardrails, now in public preview, removes the heavy lifting required by customers to build their own safeguards for image data or spend cycles with manual evaluation that can be error-prone and tedious.
Amazon Q Developer is now available in SageMaker Canvas: Starting today, you can build ML models using natural language with Amazon Q Developer, now available in Amazon SageMaker Canvas in preview. You can now get generative AI-powered assistance through the ML lifecycle, from data preparation to model deployment. With Amazon Q Developer, users of all skill levels can use natural language to access expert guidance to build high-quality ML models, accelerating innovation and time to market.
Amazon Q in QuickSight Scenarios: A new scenario analysis capability of Amazon Q in QuickSight is now available in preview. This new capability provides an AI-assisted data analysis experience that helps you make better decisions, faster. Amazon Q in QuickSight simplifies in-depth analysis with step-by-step guidance, saving hours of manual data manipulation and unlocking data-driven decision-making across your organization.
Dr. Werner Vogel!
Complexity warning signs
Declining feature velocity
Frequent escalations
Time-consuming debugging
Excessive codebase growth
Inconsistent patterns
Dependencies everywhere
Undifferentiated work
Lessons in Simplexity
Make evolvability a requirement - Evolvability is a precondition for managing complexity
Break complexity into pieces - Disaggregate into building blocks with high cohesion and well-defined APIs
Align organization to architecture - Build small teams, challenge the status quo, and encourage ownership
Organize into cells - In a complex system, you must reduce the scope of impact
Design predictable systems - Reduce the impact of uncertainty
Automate complexity - Automate everything that does not require high judgment
Building evolvable systems
Modeled on business concepts
Hidden internal details
Fine-grained interfaces
Smart endpoints
Decentralized
Independently deployable
Automated
Cloud-native design principles
Isolate failures
Highly observable
Multiple paradigms
Constant Work
Amazon Bedrock Serverless Prompt Chaining
Serverless Agentic Workflows with Amazon Bedrock
Clock Bound
Tech to the Rescue
There are of course the standard listing of announcements from the AWS team that are listed below as well. This is a good to keep for quick reference.
Analytics
Unifying data silos, Amazon SageMaker Lakehouse seamlessly integrates S3 data lakes and Redshift warehouses, enabling unified analytics and AI/ML on a single data copy through open Apache Iceberg APIs and fine-grained access controls.
Find solutions to your most critical business challenges with ease. Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets.
Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.
With expanded data sources, AWS Clean Rooms helps customers securely collaborate with their partners’ data across clouds, eliminating data movement, safeguarding sensitive information, promoting data freshness, and streamlining cross-company insights.
Application Integration
Securely share AWS resources across VPC and account boundaries with PrivateLink, VPC Lattice, EventBridge, and Step FunctionsOrchestrate hybrid workflows accessing private HTTPS endpoints – no more Lambda/SQS workarounds. EventBridge and Step Functions natively support private resources, simplifying cloud modernization.
Business Applications
Newly enhanced Amazon Connect adds generative AI, WhatsApp Business, and secure data collectionUse innovative tools like generative AI for segmentation and campaigns, WhatsApp Business, data privacy controls for chat, AI guardrails, conversational AI bot management, and enhanced analytics to elevate customer experiences securely and efficiently.
Compute
With 4x faster speed, 4x more memory bandwidth, 3x higher memory capacity than predecessors, and 30% higher floating-point operations, these instances deliver unprecedented compute power for ML training and gen AI.
Amazon EC2 P5en instances deliver up to 3,200 Gbps network bandwidth with EFAv3 for accelerating deep learning, generative AI, and HPC workloads with unmatched efficiency.
Elevate storage performance with AWS’s newest I8g instances, which deliver unparalleled speed and efficiency for I/O-intensive workloads.
New AWS I7ie instances deliver unbeatable storage performance: up to 120TB NVMe, 40% better compute performance and up to 65% better real-time storage performance.
Containers
Unify Kubernetes management across your cloud and on-premises environments with Amazon EKS Hybrid Nodes – use existing hardware while offloading control plane responsibilities to EKS for consistent operations.
With EKS Auto Mode, AWS simplifies Kubernetes cluster management, automating compute, storage, and networking, enabling higher agility and performance while reducing operational overhead.
Database
Build highly available, globally distributed apps with microsecond latencies across Regions, automatic conflict resolution, and up to 99.999% availability.
Developer Tools
Enhancing coding productivity, Amazon Q Developer agents now offer capabilities for auto-generating documentation, conducting code reviews, and creating unit tests within IDEs and GitLab.
Unlock Linux’s power with Amazon Q Developer’s transformation capabilities for .NET porting – effortlessly modernize .NET applications from Windows to cross-platform .NET in your familiar IDE.
Amazon Q Developer streamlines large-scale transformations using generative AI agents supervised by teams through a unified web experience, accelerating .NET porting, mainframe modernization, and VMware migration.
Amazon Q Developer can now help you investigate and remediate operational issues quickly from anywhere in the AWS Management Console, accelerating the troubleshooting process for operators of all experience levels.
GitLab Duo with Amazon Q streamlines software development across tasks and teams by embedding advanced AI agent capabilities into the GitLab workflows developers already know.
Education & Training
Amazon commits $100M to empower education equity initiatives, enabling socially-minded organizations to create AI-powered digital learning solutions. This aims to reach underserved students globally through innovative platforms, apps, and assistants.
Generative AI / Machine Learning
Introducing the next generation of Amazon SageMaker: The center for all your data, analytics, and AI
Unify data engineering, analytics, and generative AI in a streamlined studio with enhanced capabilities of Amazon SageMaker.
Q Developer empowers non-ML experts to build ML models using natural language, enabling organizations to innovate faster with reduced time to market.
Build responsible AI applications – Safeguard them against harmful text and image content with configurable filters and thresholds.
Amazon Bedrock enhances generative AI data analysis with multimodal processing, graph modeling, and structured querying, accelerating AI application development.
Reduce costs and latency with Amazon Bedrock Intelligent Prompt Routing and prompt caching (preview)
Route requests and cache frequently used context in prompts to reduce latency and balance performance with cost efficiency.
Discover, test, and use over 100 emerging, and specialized foundation models with the tooling, security, and governance provided by Amazon Bedrock.
Unlock efficient large model training with SageMaker HyperPod flexible training plans – find optimal compute resources and complete training within timelines and budgets.
Maximize accelerator utilization for model development with new Amazon SageMaker HyperPod task governanceEnable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.
Get started with training and fine-tuning popular publicly available foundation models, like Llama 3.1 405B, in just minutes with state-of-the-art performance.
Manage data and AI assets through a unified catalog, granular access controls, and a consistent policy enforcement. Establish trust via automation – boost productivity and innovation for data teams.
Realize visual traceability of data origins, transformations, and usage – bolstering trust, governance, and discoverability for strategic data-driven decisions.
Amazon Q Business extends productivity with generative AI-powered workflow automation capability and 50+ actions for enterprise efficiency, enabling seamless task execution across tools like ServiceNow, PagerDuty, and Asana.
New Amazon Q Business capabilities help ISVs integrate with the Amazon Q index to retrieve data from multiple sources through a single API and customize the design of their Amazon Q embedded assistant.
Amazon Nova foundation models deliver frontier intelligence and industry leading price-performance, with support for text and multimodal intelligence, multimodal fine-tuning, and high-quality images and videos.
With multi-agent collaboration on Amazon Bedrock, developers can build, deploy, and manage multiple specialized agents working together seamlessly to tackle more intricate, multi-step workflows.
Enhance conversational AI accuracy with Automated Reasoning checks – first and only gen AI safeguard that helps reduce hallucinations by encoding domain rules into verifiable policies.
Automates the process of creating a distilled model for your specific use case by generating responses from a large foundation model and fine-tunes a smaller FM with the generated responses.
Evaluate AI models and applications efficiently with Amazon Bedrock’s new LLM-as-a-judge capability for model evaluation and RAG evaluation for Knowledge Bases, offering a variety of quality and responsible AI metrics at scale.
Seamlessly access AI assistance within work applications with Amazon Q Business’s new browser extensions and integrations.
With custom connectors and reranking models, you can enhance RAG applications by enabling direct ingestion to knowledge bases without requiring a full sync, and improving response relevance through advanced reranking models.
Unleash your creativity with PartyRock’s new AI capabilities: generate images, analyze visuals, search hundreds of thousands of apps, and process multiple docs simultaneously – no coding required.
Users can now query information embedded in various types of visuals, including diagrams, infographics, charts, and other image-based content.
Management & Governance
With granular visibility into container workloads, CloudWatch Container Insights with enhanced observability for Amazon ECS enables proactive monitoring and faster troubleshooting, enhancing observability and improving application performance.
Monitor Amazon Aurora databases and gain comprehensive visibility into MySQL and PostgreSQL fleets and instances, analyze performance bottlenecks, track slow queries, set SLOs, and explore rich telemetry.
Unlock out-of-the-box OpenSearch dashboards and two additional query languages, OpenSearch SQL and PPL, for analyzing CloudWatch logs. OpenSearch customers can now analyze CloudWatch Logs without having to duplicate data.
Migration & Transfer Services
AWS DMS Schema Conversion converts up to 90% of your schema to accelerate your database migrations and reduce manual effort with the power of generative AI.
AWS Transfer Family web apps are a new resource that you can use to create a simple interface for authorized line-of-business users to access data in Amazon S3 through a customizable web browser.
Amazon S3 updates the default behavior of object upload requests with new data integrity protections that build upon S3’s existing durability posture.
Partner Network
Buy with AWS enables you to seamlessly discover and purchase products available in AWS Marketplace from AWS Partner websites using your AWS account.
Security, Identity, & Compliance
AWS introduces a new service to streamline security event response, providing automated triage, coordinated communication, and expert guidance to recover from cybersecurity threats.
AWS extends GuardDuty with AI/ML capabilities to detect complex attack sequences across workloads, applications, and data, correlating multiple security signals over time for proactive cloud security.
With only a few steps, create declarative policies and enforce desired configuration for AWS services across your organization, reducing ongoing governance overhead and providing transparency for administrators and end users.
With only a few steps, create declarative policies and enforce desired configuration for AWS services across your organization, reducing ongoing governance overhead and providing transparency for administrators and end users.
Analyze security logs without data duplication; Amazon OpenSearch Service now offers zero-ETL integration with Amazon Security Lake for efficient threat hunting and investigations.
Storage
Unlock S3 data insights effortlessly with AWS’ rich metadata capture; query objects by key, size, tags, and more using Athena, Redshift, and Spark at scale.
Amazon S3 Tables optimize tabular data storage (like transactions and sensor readings) in Apache Iceberg, enabling high-performance, low-cost queries using Athena, EMR, and Spark.
Delivering NAS capabilities with automatic data tiering among frequently accessed, infrequent, and archival storage tiers, Amazon FSx Intelligent-Tiering offers high performance up to 400K IOPS, 20 GB/s throughput, seamless integration with AWS services.
Rapidly upload large datasets to AWS at blazing speeds with the new AWS Data Transfer Terminal, secure physical locations offering high throughput connection.
Storage Browser for Amazon S3 is an open source interface component that you can add to your web applications to provide your authorized end users, such as customers, partners, and employees, with access to easily browse, upload, download, copy, and delete data in S3.

Thanks again and contact me with questions
~Justin Cook
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