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David Driggers, CTO of Cirrascale – Interview Series

Unite.AI

Enterprise-wide AI adoption faces barriers like data quality, infrastructure constraints, and high costs. How does Cirrascale address these challenges for businesses scaling AI initiatives? While Cirrascale does not offer Data Quality type services, we do partner with companies that can assist with Data issues.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

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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. It consists of three main components: Data config Specifies the dataset location and its structure.

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The Weather Company enhances MLOps with Amazon SageMaker, AWS CloudFormation, and Amazon CloudWatch

AWS Machine Learning Blog

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. The Data Quality Check part of the pipeline creates baseline statistics for the monitoring task in the inference pipeline.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. Monitoring – Continuous surveillance completes checks for drifts related to data quality, model quality, and feature attribution. Workflow B corresponds to model quality drift checks.

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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.

LLM 79
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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. Anastasia Tzeveleka is a Senior Generative AI/ML Specialist Solutions Architect at AWS.

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