AWS Machine Learning

DECEMBER 29, 2022

In this blog post, we will show you how to use both of these services together to efficiently perform analysis on massive data sets in the cloud while addressing the challenges mentioned above. In the blog today, we will be executing the following steps: Cloning the sample repository with the required packages. Solution overview.

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AWS Machine Learning

JULY 24, 2023

This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. billion parameters, trained on the WebText dataset, containing 8 million web pages. You’re likely familiar with the autocomplete suggestion feature when you search for something on Google or Amazon.

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AWS Machine Learning

DECEMBER 16, 2022

Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Alternatively, we can tackle these challenges by generating and using synthetic data. This blog post explores tabular synthetic data generation.

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AWS Machine Learning

JANUARY 26, 2024

As part of quality assurance tests, introduce synthetic security threats (such as attempting to poison training data, or attempting to extract sensitive data through malicious prompt engineering) to test out your defenses and security posture on a regular basis. The datasets you train and fine-tune your models on must also be reviewed.

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AWS Machine Learning

FEBRUARY 27, 2023

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. We use the South German Credit dataset ( South German Credit Data Set ). accuracy, auc, recall) that you define.

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AWS Machine Learning

DECEMBER 20, 2022

Fund managers invest nearly $3 billion annually in external datasets, with yearly spend growing by 20–30 percent. With AWS LCNC services, you are able to quickly subscribe to and evaluate diverse third-party datasets, preprocess data, and check their predictive power using machine learning (ML) models without writing a single piece of code.

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AWS Machine Learning

OCTOBER 19, 2023

Solution overview This solution uses Amazon Comprehend and SageMaker Data Wrangler to automatically redact PII data from a sample dataset. You can use SageMaker Data Wrangler to simplify and streamline dataset preprocessing and feature engineering by either using built-in, no-code transformations or customizing with your own Python scripts.

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