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Amazon Web Services (AWS) announced four new innovations for Amazon SageMaker AI at AWS re:Invent, aimed at enhancing generative AI model development. The updates include three new Amazon SageMaker HyperPod capabilities and integration of AWS Partner AI applications.

Key features include: 30+ curated model training recipes for popular models like Llama and Mistral, flexible training plans for better resource management, and task governance that can reduce model development costs by up to 40%. The platform now also offers direct integration with partner applications like Comet, Deepchecks, Fiddler AI, and Lakera.

Major companies including Articul8, Commonwealth Bank of Australia, Fidelity, and Salesforce are already utilizing these new capabilities to accelerate their generative AI development.

Amazon Web Services (AWS) announced major expansions to Amazon Bedrock, its managed service for generative AI applications. The updates include: access to over 100 new models through Amazon Bedrock Marketplace, new models from Luma AI, poolside, and Stability AI, and enhanced inference optimization tools.

Key innovations include prompt caching (reducing costs by up to 90% and latency by up to 85%), Intelligent Prompt Routing (cutting costs by up to 30%), and new data capabilities. The service introduced support for structured data retrieval and GraphRAG in Knowledge Bases, plus Amazon Bedrock Data Automation for transforming unstructured data.

The platform has seen significant growth, with its customer base increasing 4.7x in the last year. Major companies including Adobe, BMW Group, and Zendesk are already implementing these new features.

Amazon Web Services (AWS) and poolside announced a multi-year agreement to make poolside's generative AI Assistant and foundation models available on Amazon Bedrock and Amazon EC2. The partnership allows enterprise customers to customize poolside's AI tools using their own data while leveraging AWS's security and performance features.

poolside will utilize AWS Trainium chips for model inference, optimizing price performance. The company's foundation models, malibu and point, are specifically designed for software development, with malibu focusing on complex engineering challenges and point specializing in rapid code completion. The solution enables secure deployment within customer firewalls, with no data shared back to poolside.

Atlassian (NASDAQ: TEAM) and Amazon Web Services (AWS) announced a multi-year strategic collaboration agreement to accelerate enterprise cloud migration and enhance AI capabilities. The partnership aims to migrate millions of users from Atlassian's Data Center business, which generates over $1 billion in annual revenue, to Atlassian Cloud.

The collaboration includes establishing a Cloud Center of Excellence to streamline enterprise migrations, potentially reducing migration time by 50%. Atlassian Cloud, powered by AWS, currently serves over 300,000 customers and features AI-powered capabilities through Atlassian Intelligence, which has seen nearly tenfold increase in usage this year.

The agreement also focuses on enhancing the Forge development platform with AWS services, including generative AI technologies like Amazon Bedrock for app development.

XtalPi, a global leader in AI and robotics drug-discovery technologies, has selected AWS as its strategic cloud provider. The company leverages AWS services to help pharmaceutical companies expedite drug discovery and development. Using Amazon Bedrock, XtalPi's PatSight platform can analyze thousands of compound structures in an hour, achieving over 95% accuracy in patent coverage determination.

The company utilizes multiple AWS services including Amazon Q Developer for software development, Amazon EKS for container deployment, and Amazon SageMaker for machine learning model creation. Through AWS's solutions, XtalPi has reduced operational and maintenance costs by more than 50% while enhancing research efficiency.

Amazon Web Services (AWS) announced new capabilities for Amazon Q Business, its AI-powered work assistant. The key updates include:

- Integration between Amazon Q Business and QuickSight for unified data insights across structured and unstructured sources
- More than 50 new actions for popular business applications like Asana and Teams
- New automation capability using AI agents to execute complex workflows
- Enhanced cross-app generative AI experiences through unified enterprise data indexing

Major partners including Asana, GE HealthCare, Kepler, and Zoom are implementing these features. The platform has already shown significant impact, helping AWS Sales team generate over 100,000 account summaries and saving developers more than 450,000 hours on technical investigations. The new automation capability is scheduled for release in 2025.

Amazon Web Services (AWS) announced new enhancements to Amazon Q Developer, their generative AI-powered assistant for software development. The updates include automated capabilities for unit testing, documentation, code reviews, and operational troubleshooting.

Key features include autonomous identification and generation of unit tests, automated documentation maintenance, streamlined code review processes checking for quality and security vulnerabilities, and operational issue resolution leveraging AWS expertise. The system promises to speed up various software development tasks by up to 80% and provides the highest reported code acceptance rate for multi-line code suggestions.

Major companies including Accenture, Amazon's Kindle, Prime Video, Boomi, and Deloitte are already using Amazon Q Developer, reporting significant improvements in productivity. For instance, Boomi anticipates 25% reduction in manual testing time, while Deloitte reports 30% increase in development speed.

AWS has announced the next generation of Amazon SageMaker, unifying data, analytics, and AI capabilities into one integrated platform. The update introduces three major components:

1. SageMaker Unified Studio: A single environment combining SQL analytics, data processing, and ML model development tools, assisted by Amazon Q Developer.

2. SageMaker Catalog: Built on Amazon DataZone, it provides governance capabilities and access control for data and AI assets.

3. SageMaker Lakehouse: Unifies data across S3 data lakes, Redshift warehouses, and federated sources, supporting Apache Iceberg compatibility.

The platform also introduces new zero-ETL integrations with SaaS applications like Zendesk and SAP, simplifying data access for analytics and ML without complex pipelines. Companies including NatWest Group and Roche are already exploring these capabilities, with NatWest anticipating a 50% reduction in data access time and Roche expecting 40% reduction in data processing time.

AWS announced new capabilities for Amazon Q Developer, an AI-powered assistant designed to accelerate legacy application migration and modernization projects. The tool offers three main transformation capabilities:

1. Windows .NET modernization: Speeds up migration to Linux up to 4x faster, potentially reducing licensing costs by 40%.

2. VMware workload transformation: Converts on-premises configurations to AWS equivalents in hours instead of weeks.

3. Mainframe modernization: Streamlines IBM z/OS mainframe modernization through automated code analysis and documentation.

The system uses AI-powered agents to automate tasks like code analysis, planning, and testing. Notable success includes AWS's internal migration of Java applications, saving over 4,500 years of development work and $260 million in annual costs. Companies like ADP, Cognizant, and Signaturit Group are already utilizing these capabilities to accelerate their transformation projects.