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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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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. A provisioned or serverless Amazon Redshift data warehouse.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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How Booking.com modernized its ML experimentation framework with Amazon SageMaker

AWS Machine Learning Blog

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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6 AI tools revolutionizing data analysis: Unleashing the best in business

Data Science Dojo

Scikit-learn can be used for a variety of data analysis tasks, including: Classification Regression Clustering Dimensionality reduction Feature selection Leveraging Scikit-learn in data analysis projects Scikit-learn can be used in a variety of data analysis projects.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

Let us now look at the key differences starting with their definitions and the type of data they use. Definition of Supervised Learning and Unsupervised Learning Supervised learning is a process where an ML model is trained using labeled data. In this case, every data point has both input and output values already defined.

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Implement a custom AutoML job using pre-selected algorithms in Amazon SageMaker Automatic Model Tuning

AWS Machine Learning Blog

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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