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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

One aspect of this data preparation is feature engineering. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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Transaction Risk API- What value does it bring to the market? (Blog 3 of 3)

Whitepages Pro

Our latest product innovation, Transaction Risk API , was specifically built for easy integration into sophisticated machine learning (ML) models and is designed to help eCommerce merchants, marketplaces, payment processors, and others manage payment fraud. Understanding market requirements. Value of Transaction Risk API.

APIs 40
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T-Mobile US, Inc. uses artificial intelligence through Amazon Transcribe and Amazon Translate to deliver voicemail in the language of their customers’ choice

AWS Machine Learning

This post is co-authored by Dhurjati Brahma, Senior Systems Architect at T-Mobile US, Inc and Jim Chao, Principal Engineer/Architect at T-Mobile US, Inc and Nicholas Zellerhoff Associate Systems Architect at T-Mobile US, Inc. In addition, market research based on U.S. T-Mobile US, Inc.

APIs 90
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Streamline Sales Processes with Enterprise CPQ

Cincom

Integration with Existing Systems: APIs facilitate data sharing between CPQ and other core platforms like CRM, ERP, accounting, e-commerce, and more. These Configure, Price, Quote engines can encode deeply technical manufacturing specifications required for specialized equipment. What Makes Cincom’s CPQ Solutions Stand Out?

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning

Our models are the building blocks of other models where we generate a list of live betting markets, include spread, total, win probability, next score type, next team to score, and more. They use the DJL PyTorch engine to initialize the model predictor. The architecture of DJL is engine agnostic.

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5 Tips for Planning the Growth of your Support Team in the New Year

Nicereply

This can include anything from knowing how many people you need to hire and knowing exactly what metrics you need to hit, to determining if there are new tools that you’ll need to use in order to a ccomplish your goals. If so, what are some levers you might be able to pull to shift that metric, too?

Metrics 98
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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022. The independent nature of each individual metric transformation also makes Lambda, which is a stateless service on its own, a good fit for this problem.