Tag: HAQM SageMaker

It’s mid-March and in the United States that can mean only one thing – it’s time for March Madness! Every year countless people fill out a bracket trying to pick which college basketball team will take it all. Do you have a favorite team to win in 2018? In this blog post, we’ll show you […]

The training and learning process of deep learning (DL) models can be expensive and time consuming. It’s important for data scientists to monitor the model metrics, such as the training accuracy, training loss, validation accuracy, and validation loss, and make informed decisions based on those metrics. In this blog post, I’ll show you how to […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. Today we are excited to announce that you can […]

Introduced at re:Invent 2017, HAQM SageMaker provides a serverless data science environment to build, train, and deploy machine learning models at scale. Customers also have the ability to work with frameworks they find most familiar, such as Scikit learn. In this blog post, we’ll accomplish two goals: First, we’ll give you a high-level overview of […]

HAQM SageMaker pre-built deep learning framework containers now support TensorFlow 1.5 and Apache MXNet 1.0, both of which take advantage of CUDA 9 optimizations for faster performance on SageMaker ml.p3 instances. In addition to performance benefits, this provides access to updated features such as Eager execution in TensorFlow and advanced indexing for NDArrays in MXNet. More […]

Machine learning (ML) methods for the field of computational chemistry are growing at an accelerated rate. Easy access to open-source solvers (such as TensorFlow and Apache MXNet), toolkits (such as RDKit cheminformatics software), and open-scientific initiatives (such as DeepChem) makes it easy to use these frameworks in daily research. In the field of chemical informatics, many […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. We are excited to launch a new feature for […]

Today we’re launching HAQM SageMaker BlazingText as the latest built-in algorithm for HAQM 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 HAQM SageMaker users on: Single CPU instances (like the […]

HAQM 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 […]

Just as the goal of HAQM AI is to democratize machine learning with the development of platforms such as HAQM 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.