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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. These capabilities are essential for demonstrating compliance with regulatory standards and ensuring transparency and accountability in AI/ML workflows.

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Get started with Amazon Titan Text Embeddings V2: A new state-of-the-art embeddings model on Amazon Bedrock

AWS Machine Learning

We published a follow-up post on January 31, 2024, and provided code examples using AWS SDKs and LangChain, showcasing a Streamlit semantic search app. A common way to select an embedding model (or any model) is to look at public benchmarks; an accepted benchmark for measuring embedding quality is the MTEB leaderboard.

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The executive’s guide to generative AI for sustainability

AWS Machine Learning

Figure 1: Examples of generative AI for sustainability use cases across the value chain According to KPMG’s 2024 ESG Organization Survey , investment in ESG capabilities is another top priority for executives as organizations face increasing regulatory pressure to disclose information about ESG impacts, risks, and opportunities.

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Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning

Policies and regulations like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), and California Consumer Privacy Act (CCPA) put guardrails on sharing data from the medical domain, especially patient data.