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Training a custom single class object detection model with Amazon Rekognition Custom Labels

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Customers often need to identify single objects in images; for example, to identify their company's logo, find a specific industrial or agricultural defect, or locate a specific event, like hurricanes, in satellite scans. In this post, we showcase how to train a custom model to detect a single object using Amazon Rekognition Custom Labels. Amazon Rekognition is a fully managed service that provides computer vision (CV) capabilities for analyzing images and video at scale, using deep learning technology without requiring machine learning (ML) expertise. Amazon Rekognition Custom Labels lets you extend the detection and classification capabilities of the Amazon Rekognition pre-trained APIs by using data to train a custom CV model specific to your business needs. With the latest update to support single object training, Amazon Rekognition Custom Labels now lets you create a custom object detection model with single object classes.


Securing Amazon Comprehend API calls with AWS PrivateLink

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Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights in text. You can use Amazon Comprehend to analyze text documents and identify insights such as sentiment, people, brands, places, and topics in text. Using AWS PrivateLink, you can access Amazon Comprehend easily and securely by keeping your network traffic within the AWS network, while significantly simplifying your internal network architecture. It enables you to privately access Amazon Comprehend APIs from your VPC in a scalable manner by using interface VPC endpoints.


Chatbot for Ecommerce: Boost Your Online Sales With The Help of A Chatbot

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A chatbot can benefit the biggest or the smallest eCommerce company. Whether you're selling clothes, shoes, makeup, electronics, art & crafts, or furniture, having a chatbot is a great investment for your store. Big brands benefit from chatbots because it allows them to engage with numerous customers in a timely and efficient manner. At the same time, small stores that cannot hire several employees can compete with bigger businesses by providing immediate service. Having a chatbot can help increase your revenue because it solves different customer problems that often lead to cart abandonment.


Brick and mortar's best hope? Robots, many now believe

ZDNet

Through a pane of clear plastic, speaking through a mask, a checkout clerk at a grocery chain told my wife she was feeling sick yesterday. My wife asked (and I'm imagining her taking a big step back as she did) if she'd told her manager. The clerk replied she had but she was out of sick days and couldn't afford to lose the pay. That story is true, and it's horrifying -- both for the risk of outbreak it suggests and for the complicated labor realities it betrays. It's also an anecdotal illustration of one more reason automation is coming to grocery stores, and fast.


How Artificial Intelligence is Building The Future of eCommerce

#artificialintelligence

While the whole planet was frozen by the coronavirus pandemic, offline stores found they couldn't compete with even the smallest online stores when people's lifestyles were limited by their homes or neighborhoods. But those who have just started online sales this year will quickly find out what to do to sell efficiently on the internet. This is why the overall competition will rise. Wondering how you can gain a foothold at this moment? Take a look at modern technologies – artificial intelligence (AI), machine learning (ML), and big data analysis.


Get over 50 hours of training in machine learning for under £30

Mashable

TL;DR: The Machine Learning Master Class bundle is on sale for £29.85 as of August 4, saving you 91% on list price. Thanks to developing artificial intelligence technologies, computer are smarter than ever before. Along with those technologies has come a relatively new category of computer science called machine learning, or ML. Similar to statistics, ML involves computer systems that utilise algorithms to automatically learn about data, recognise patterns, and make decisions, all without outside intervention or explicit directions from human beings. In the real world, you can find it being used in smart assistants like Siri and the Amazon Echo, in online fraud detection services, in the facial recognition feature that identifies photos of you on Facebook, and more recently, in Tesla's self-driving car.


Image Classifier -- Zalando Clothing Store using Monk Library

#artificialintelligence

This tutorial is about image classification on the Zalando Clothing Store dataset using Monk Library. In this dataset, there are high-res, color, and diverse images of clothing and models. This is the best tool to use for competitions held in platforms like Kaggle, Codalab, HackerEarth, AiCrowd, etc. For other ways to install, visit Monk Library. This section is to give you a demo of this classifier before getting into further details.


This month in AWS Machine Learning: July 2020 edition

#artificialintelligence

Every day there is something new going on in the world of AWS Machine Learning--from launches to new use cases like posture detection to interactive trainings like the AWS Power Hour: Machine Learning on Twitch. Check back at the end of each month for the latest roundup. As models become more sophisticated, AWS customers are increasingly applying machine learning (ML) prediction to video content, whether that's in media and entertainment, autonomous driving, or more. Want more news about developments in ML? Check out the following stories: Also, if you missed it, see the Amazon Augmented AI (Amazon A2I) Tech Talk to learn how you can implement human reviews to review your ML predictions from Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon SageMaker, and other AWS AI/ ML services. See you next month for more on AWS ML! Laura Jones is a product marketing lead for AWS AI/ML where she focuses on sharing the stories of AWS's customers and educating organizations on the impact of machine learning.


Enhancing recommendation filters by filtering on item metadata with Amazon Personalize

#artificialintelligence

We're pleased to announce enhancements to recommendation filters in Amazon Personalize, which provide you greater control on recommendations your users receive by allowing you to exclude or include items to recommend based on criteria that you define. For example, when recommending products for your e-retail store, you can exclude unavailable items from recommendations. If you're recommending videos to users, you can choose to only recommend premium content if the user is in a particular subscription tier. You typically address this by writing custom code to implement their business rules, but you can now save time and streamline your architectures by using recommendation filters in Amazon Personalize. Based on over 20 years of personalization experience, Amazon Personalize enables you to improve customer engagement by powering personalized product and content recommendations and targeted marketing promotions.


Code-free machine learning: AutoML with AutoGluon, Amazon SageMaker, and AWS Lambda

#artificialintelligence

One of AWS's goals is to put machine learning (ML) in the hands of every developer. With the open-source AutoML library AutoGluon, deployed using Amazon SageMaker and AWS Lambda, we can take this a step further, putting ML in the hands of anyone who wants to make predictions based on data--no prior programming or data science expertise required. AutoGluon automates ML for real-world applications involving image, text, and tabular datasets. AutoGluon trains multiple ML models to predict a particular feature value (the target value) based on the values of other features for a given observation. During training, the models learn by comparing their predicted target values to the actual target values available in the training data, using appropriate algorithms to improve their predictions accordingly.