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Customized model monitoring for near real-time batch inference with Amazon SageMaker

AWS Machine Learning Blog

Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling. Ajay Raghunathan is a Machine Learning Engineer at AWS.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. This allows you to keep track of your ML experiments.

LLM 103
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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. Runs are executions of some piece of data science code and record metadata and generated artifacts.

ML 97
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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

Regulatory compliance By integrating the extracted insights and recommendations into clinical trial management systems and EHRs, this approach facilitates compliance with regulatory requirements for data capture, adverse event reporting, and trial monitoring. He helps customers implement big data, machine learning, and analytics solutions.

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

AWS Machine Learning Blog

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. Robert, you can go first.