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Tag: AWS re:Invent

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Amazon Personalize – Real-Time Personalization and Recommendation for Everyone

Machine learning definitely offers a wide range of exciting topics to work on, but there’s nothing quite like personalization and recommendation. At first glance, matching users to items that they may like sounds like a simple problem. However, the task of developing an efficient recommender system is challenging. Years ago, Netflix even ran a movie […]

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AWS DeepRacer – Go Hands-On with Reinforcement Learning at re:Invent

Reinforcement Learning is a type of machine learning that works when an “agent” is allowed to act on a trial-and-error basis within an interactive environment, using feedback from those actions to learn over time in order to reach a predetermined goal or to maximize some type of score or reward. This stands in contrast to […]

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Amazon SageMaker RL – Managed Reinforcement Learning with Amazon SageMaker

In the last few years, machine learning (ML) has generated a lot of excitement. Indeed, from medical image analysis to self-driving trucks, the list of complex tasks that ML models can successfully accomplish keeps growing, but what makes these models so smart? In a nutshell, you can train a model in several different ways of which […]

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NEW – Machine Learning algorithms and model packages now available in AWS Marketplace

At AWS, our mission is to put machine learning in the hands of every developer. That’s why in 2017 we launched . Since then it has become one of the fastest growing services in AWS history, used by thousands of customers globally. Customers using Amazon SageMaker can use optimized algorithms offered in Amazon SageMaker, to […]

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Amazon SageMaker Ground Truth – Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70%

In 1959, Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed”. However, there is no deus ex machina: the learning process requires an algorithm (“how to learn”) and a training dataset (“what to learn from”). Today, most machine learning tasks use a technique […]

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Amazon Elastic Inference – GPU-Powered Deep Learning Inference Acceleration

One of the reasons for the recent progress of Artificial Intelligence and Deep Learning is the fantastic computing capabilities of Graphics Processing Units (GPU). About ten years ago, researchers learned how to harness their massive hardware parallelism for Machine Learning and High Performance Computing: curious minds will enjoy the seminal paper (PDF) published in 2009 […]

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Amazon DynamoDB On-Demand – No Capacity Planning and Pay-Per-Request Pricing

Just a few years ago, creating a database that could support your business at any scale while providing consistent low latency was a daunting task. That changed for me in 2012 while reading Werner Vogels’ blog post announcing Amazon DynamoDB (it was a few months before I joined AWS). was built on the principles in the […]

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New – Amazon FSx for Lustre

A pebibyte (PiB – 1,125,899,906,842,624 bytes) is an impressive amount of data, slightly less than half of the estimated memory capacity of a human brain. Data lakes, High-Performance Computing (HPC), and Electronic Design Automation (EDA) applications traditionally work at this scale, as do more recent data-intensive applications such as Machine Learning and media processing. Amazon […]

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New – Amazon FSx for Windows File Server – Fast, Fully Managed, and Secure

Organizations that want to run Windows applications on the cloud are commonly looking for network file storage that’s fully compatible with their applications and their Windows environments. For example, enterprises use Active Directory for identification and Windows Access Control Lists for fine-grained control over access to folders and files, and their applications typically rely on […]

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New – Amazon Kinesis Data Analytics for Java

Customers are using to collect, process, and analyze real-time streaming data. In this way, they can react quickly to new information from their business, their infrastructure, or their customers. For example, Epic Games ingests more than 1.5 million game events per second for its popular online game, Fortnite. With Amazon Kinesis Data Analytics you can process data […]

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