Optimal pricing for maximum profit using Amazon SageMaker

This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with the

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Shared by AWS Machine Learning August 5, 2022

Introducing the Google Universal Image Embedding Challenge

Posted by Bingyi Cao, Software Engineer, Google Research, and Mário Lipovský, Software Engineer, Google Lens Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction. One challenging type of computer vision problem is instance-level recognition (ILR) — given an

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Shared by Google AI Technology August 4, 2022

Promote feature discovery and reuse across your organization using Amazon SageMaker Feature Store and its feature-level metadata capability

Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working on

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Shared by AWS Machine Learning August 4, 2022

Amazon Comprehend announces lower annotation limits for custom entity recognition

Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface,

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Shared by AWS Machine Learning August 4, 2022

Building Efficient Multiple Visual Domain Models with Multi-path Neural Architecture Search

Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual tasks

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Shared by Google AI Technology August 3, 2022

Efficient Sequence Modeling for On-Device ML

Posted by Arun Kandoor, Software Engineer, Google Research The increasing demand for machine learning (ML) model inference on-device (for mobile devices, tablets, etc.) is driven by the rise of compute-intensive applications, the need to keep certain data on device for privacy and security reasons, and the desire to provide services

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Shared by Google AI Technology August 3, 2022

Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store

Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is

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Shared by AWS Machine Learning August 2, 2022

The value of pull-based community meetings

Don’t just run your community meetings as presentations; instead engage in real multi-way dialogue around important questions. Brown bag lunch, by Gloria, on Flickr I have blogged several times about Push and Pull in Knowledge Management (aka Knowledge Supply and demand); about the dangers of focusing only on Push (such a common

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Shared by Nick Milton August 1, 2022