Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models

Favorite This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both

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Shared by AWS Machine Learning September 24, 2022

Amazon Comprehend Targeted Sentiment adds synchronous support

Favorite Earlier this year, Amazon Comprehend, a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text, launched the Targeted Sentiment feature. With Targeted Sentiment, you can identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated

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Shared by AWS Machine Learning September 22, 2022

Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

Favorite Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. These algorithms and models can be used for both supervised and unsupervised learning. They can process various

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Shared by AWS Machine Learning September 21, 2022

Configure a custom Amazon S3 query output location and data retention policy for Amazon Athena data sources in Amazon SageMaker Data Wrangler

Favorite Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature

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Shared by AWS Machine Learning September 21, 2022