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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. An AWS Region from the list of batch inference supported Regions for Amazon Bedrock.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

As successful proof-of-concepts transition into production, organizations are increasingly in need of enterprise scalable solutions. For details on all the fields and providing configuration of various vector stores supported by Knowledge Bases for Amazon Bedrock, refer to AWS::Bedrock::KnowledgeBase.

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Scalable training platform with Amazon SageMaker HyperPod for innovation: a video generation case study

AWS Machine Learning - AI

To accelerate iteration and innovation in this field, sufficient computing resources and a scalable platform are essential. With these capabilities, customers are adopting SageMaker HyperPod as their innovation platform for more resilient and performant model training, enabling them to build state-of-the-art models faster.

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High-performance computing on AWS

Xebia

How does High-Performance Computing on AWS differ from regular computing? For this HPC will bring massive parallel computing, cluster and workload managers and high-performance components to the table. It provides a powerful and scalable platform for executing large-scale batch jobs with minimal setup and management overhead.

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SAP publishes open source manifesto

CIO

It arrives alongside the announcement of SAP’s Open Reference Architecture project as part of the EU’s IPCEI-CIS initiative. Organizations are choosing these platforms based on effective cost, performance, and scalability.”

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning - AI

We present the reinforcement learning process and the benchmarking results to demonstrate the LLM performance improvement. You can refer to further explanations in the following resources:** * ARS GEN 10.0/05.01.02. Design Criteria & Appendices/Performance Package AR Sortable Design Criteria v20.1.1.pdf

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Build scalable Low-Code backends with Booster

The Agile Monkey

However, these tools may not be suitable for more complex data or situations requiring scalability and robust business logic. In short, Booster is a Low-Code TypeScript framework that allows you to quickly and easily create a backend application in the cloud that is highly efficient, scalable, and reliable. WTF is Booster?