Amazon SageMaker HyperPod recipes help customers 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...
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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...
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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.
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Q Developer empowers non-ML experts to build ML models using natural language, enabling organizations to innovate faster with reduced time to market.
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Build responsible AI applications - Safeguard them against harmful text and image content with configurable filters and thresholds.
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Amazon Bedrock enhances generative AI data analysis with multimodal processing, graph modeling, and structured querying, accelerating AI application development.
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Route requests and cache frequently used context in prompts to reduce latency and balance performance with cost efficiency.
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Discover, test, and use over 100 emerging, and specialized foundation models with the tooling, security, and governance provided by Amazon Bedrock.
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Unlock efficient large model training with SageMaker HyperPod flexible training plans - find optimal compute resources and complete training within timelines and budgets.
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Enable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.
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Connect, discover, and govern data across silos with Amazon SageMaker Lakehouse's new data catalog and permissions capabilities, enabling centralized access and fine-grained controls.
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Simplify data replication and ingestion from applications such as Salesforce, SAP, ServiceNow, and Zendesk, to Amazon SageMaker Lakehouse and Amazon Redshift.
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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...
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Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.
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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.
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Realize visual traceability of data origins, transformations, and usage - bolstering trust, governance, and discoverability for strategic data-driven decisions.
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Unify data engineering, analytics, and generative AI in a streamlined studio with enhanced capabilities of Amazon SageMaker.
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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...
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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...
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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.
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