Generative Data Intelligence

Tag: Sample dataset

Train and host a computer vision model for tampering detection on Amazon SageMaker: Part 2 | Amazon Web Services

In the first part of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at...

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Use foundation models to improve model accuracy with Amazon SageMaker | Amazon Web Services

Photo by Scott Webb on Unsplash Determining the value of housing is a classic example of using machine learning (ML). A...

Announcing Rekogniton Custom Moderation: Enhance accuracy of pre-trained Rekognition moderation models with your data | Amazon Web Services

Companies increasingly rely on user-generated images and videos for engagement. From ecommerce platforms encouraging customers to share product images to social media companies promoting...

Automatically redact PII for machine learning using Amazon SageMaker Data Wrangler | Amazon Web Services

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many...

Prepare your data for Amazon Personalize with Amazon SageMaker Data Wrangler | Amazon Web Services

A recommendation engine is only as good as the data used to prepare it. Transforming raw data into a format that is suitable for...

Machine learning with decentralized training data using federated learning on Amazon SageMaker | Amazon Web Services

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large...

Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler | Amazon Web Services

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. You can...

Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML | Amazon Web Services

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration...

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics | Amazon Web Services

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and...

Build an email spam detector using Amazon SageMaker | Amazon Web Services

Spam emails, also known as junk mail, are sent to a large number of users at once and often contain scams, phishing content, or...

Predict vehicle fleet failure probability using Amazon SageMaker Jumpstart | Amazon Web Services

Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle...

Announcing the updated Microsoft SharePoint connector (V2.0) for Amazon Kendra | Amazon Web Services

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source...

Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making...

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