AWS News Blog

Machine learning (ML) is split in two distinct phases: training and inference. Training deals with building the model, i.e. running a ML algorithm on a dataset in order to identify meaningful patterns. This often requires large amounts of storage and computing power, making the cloud a natural place to train ML jobs with services such […]

The capacity to foresee the future would be an incredible superpower. At AWS, we can’t give you that, but we can help you use machine learning to forecast time series in a few steps. The goal of time series forecasting is to predict future values of time-dependent data such as weekly sales, daily inventory levels, […]

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 […]

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 […]

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 […]

At AWS, our mission is to put machine learning in the hands of every developer. That’s why in 2017 we launched Amazon SageMaker. 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, […]

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 […]

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 […]

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). DynamoDB was built on the principles in […]

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 […]