AWS Machine Learning Blog

Apache MXNet is an open source library developers can use to build, train, and re-use deep learning networks. In this  blog post, I’ll show you to speed up inference by using the NNPACK library. Indeed, when GPU inference is not available, adding NNPACK to Apache MXNet might be a simple option to extract more performance […]

Today we’re launching Amazon SageMaker BlazingText as the latest built-in algorithm for Amazon SageMaker. BlazingText is an unsupervised learning algorithm for generating Word2Vec embeddings. These are dense vector representations of words in large corpora. We’re excited to make BlazingText, the fastest implementation of Word2Vec, available to Amazon SageMaker users on: Single CPU instances (like the […]

In this blog post we’ll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness. These conversations are a low-cost way to acquire leads, improve website traffic, develop […]

Amazon SageMaker uses throwaway keys, also called transient keys, to encrypt the ML General Purpose storage volumes attached to training and hosting EC2 instances. Because these keys are used only to encrypt the ML storage volumes and are then immediately discarded, the volumes can safely be used to store confidential data. Volumes can be accessed […]

This is a guest post by AWS Community Hero Cyrus Wong. Our institution (IVE) here in Hong Kong has begun experimenting with Amazon Lex in teaching, research, and healthcare. We have many non-technical employees, such as English teachers in IVE and therapists from IVE Childcare, Elderly and Community Services Discipline, who don’t have the technical […]

Just as the goal of Amazon AI is to democratize machine learning with the development of platforms such as Amazon SageMaker, the goal of fast.ai is to level the educational playing field so that anyone can pick up machine learning and be productive. The fast.ai tagline is “Making neural nets uncool again.” This is not a play to decrease the popularity of deep neural networks, but instead to broaden their appeal and accessibility beyond the academic elites who have dominated the research in this area.

AWS customers have been requesting a way to log activity in Amazon SageMaker, to help you meet your governance and compliance needs. I’m happy to announce that Amazon SageMaker is now integrated with AWS CloudTrail, a service that enables you to log, continuously monitor, and retain account information related to Amazon SageMaker API activity. Amazon […]

Today we are launching Amazon SageMaker DeepAR as the latest built-in algorithm for Amazon SageMaker. DeepAR is a supervised learning algorithm for time series forecasting that uses recurrent neural networks (RNN) to produce both point and probabilistic forecasts. We’re excited to give developers access to this scalable, highly accurate forecasting algorithm that drives mission-critical decisions within Amazon. Just as […]

This blog post was last reviewed August, 2022. Introduced at AWS re:Invent in 2017, Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the important parts of Amazon SageMaker is the powerful Jupyter notebook interface, which can be used to build models. You can enhance the Amazon SageMaker […]

Deep learning has revolutionized how we process and handle real-world data. There are many types of deep learning applications, including applications to organize a user’s photo archive, make book recommendations, detect fraudulent behavior, and perceive the world around an autonomous vehicle. In this post, we’ll show you step-by-step how to use your own custom-trained models […]