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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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Predicting the 2024 U.S. Presidential Election Winner Using Machine Learning

Towards AI

Predicting the elections, however, presents challenges unique to it, such as the dynamic nature of voter preferences, non-linear interactions, and latent biases in the data. The points to cover in this article are as follows: Generating synthetic data to illustrate ML modelling for election outcomes.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. In addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.”

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Data4ML Preparation Guidelines (Beyond The Basics)

Towards AI

Last Updated on November 9, 2024 by Editorial Team Author(s): Houssem Ben Braiek Originally published on Towards AI. Data preparation isn’t just a part of the ML engineering process — it’s the heart of it. Data is a key differentiator in ML projects (more on this in my blog post below).

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Large Language Models: A Self-Study Roadmap

Flipboard

billion in 2024 to USD 36.1 Hugging Face Tutorial (2024) - This comprehensive guide covers various NLP tasks, including building a sentiment analysis model with Hugging Face (Recommended). It covers the entire process, from data preparation to model training and evaluation, enabling viewers to adapt LLMs for specific tasks or domains.

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Modernize and migrate on-premises fraud detection machine learning workflows to Amazon SageMaker

AWS Machine Learning Blog

In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker. Businesses need for fraud detection models ML has proven to be an effective approach in fraud detection compared to traditional approaches.

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Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency

AWS Machine Learning Blog

Preparing your data Effective data preparation is crucial for successful distillation of agent function calling capabilities. Amazon Bedrock provides two primary methods for preparing your training data: uploading JSONL files to Amazon S3 or using historical invocation logs.

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