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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units. AI/ML specification report generation for regulatory compliance AWS maintains compliance certifications for various industry standards and regulations.

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Gemma is now available in Amazon SageMaker JumpStart 

AWS Machine Learning

Now, let’s look at latency and throughput performance benchmarking for model serving with the default JumpStart deployment configuration. For more information on how to consider this information and adjust deployment configurations for your specific use case, see Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart.

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Best practices to build generative AI applications on AWS

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API. Kojima et al. 2022) introduced an idea of zero-shot CoT by using FMs’ untapped zero-shot capabilities.

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Evaluate large language models for quality and responsibility

AWS Machine Learning

Customers have to leave their development environment to use academic tools and benchmarking sites, which require highly-specialized knowledge. FM evaluations provides actionable insights from industry-standard science, that could be extended to support customer-specific use cases.