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Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

The solution is then able to make predictions on the rest of the training data, and route lower-confidence results for human review. In this post, we describe our design and implementation of the solution, best practices, and the key components of the system architecture.

AWS 90
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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Data and workflow orchestration: Ensuring efficient data pipeline management and scalable workflows for LLM performance. Observability tools: Use platforms that offer comprehensive observability into LLM performance, including functional logs (prompt-completion pairs) and operational metrics (system health, usage statistics).