AWS Machine Learning Blog
Author: Julien Simon
As an Artificial Intelligence & Machine Learning Evangelist for EMEA, Julien focuses on helping developers and enterprises bring their ideas to life.
AWS and Hugging Face collaborate to simplify and accelerate adoption of Natural Language Processing models
Just like computer vision a few years ago, the decade-old field of natural language processing (NLP) is experiencing a fascinating renaissance. Not a month goes by without a new breakthrough! Indeed, thanks to the scalability and cost-efficiency of cloud-based infrastructure, researchers are finally able to train complex deep learning models on very large text datasets, […]
Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the […]
Train and deploy Keras models with TensorFlow and Apache MXNet on Amazon SageMaker
Keras is a popular and well-documented open source library for deep learning, while Amazon SageMaker provides you with easy tools to train and optimize machine learning models. Until now, you had to build a custom container to use both, but Keras is now part of the built-in TensorFlow environments for TensorFlow and Apache MXNet. Not […]
Build a movie recommender with factorization machines on Amazon SageMaker
Factorization machines can be used for classification or regression and are much more computationally efficient on large sparse data sets than traditional algorithms like linear regression. This property is why FM are widely used for recommendation.
Use pre-trained models with Apache MXNet
In this blog post, I’ll show you how to use multiple pre-trained models with Apache MXNet. Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the blog post, even though these models have been trained on the same data set and optimized for maximum accuracy, they do behave slightly differently on specific images.
Speeding up Apache MXNet using the NNPACK library
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 […]