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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

ML 113
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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

AWS 112
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Revealing the Secrets of Startup Success: A Venture Capital Investments Challenge

Ocean Protocol

Participants demonstrated outstanding ability in utilizing ML and AI to examine and predict startup success within the venture capital landscape and refine investment strategies. Annual Increase in Funding Amounts Since 2010, the average amount raised per startup funding round has increased by 15% annually.

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Unpacking and Utilizing Vertex with Google Earth Engine for Machine Learning.

Towards AI

I am referring to Vertex, the new machine learning platform that can help you train and deploy ML models and AI applications, and customize large language models (LLMs) for use in your AI-powered applications which is a new product set to be a game changer in the AI tech race. What is Google Earth Engine? What is Vertex?

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Improving air quality with generative AI

AWS Machine Learning Blog

The attempt is disadvantaged by the current focus on data cleaning, diverting valuable skills away from building ML models for sensor calibration. Qiong (Jo) Zhang , PhD, is a Senior Partner Solutions Architect at AWS, specializing in AI/ML. She holds 30+ patents and has co-authored 100+ journal/conference papers.

AWS 119
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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning Blog

Amazon SageMaker Canvas Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. Through Atlas Data Federation, data is extracted into Amazon S3 bucket.