AWS News Blog
Amazon SageMaker Edge Manager Simplifies Operating Machine Learning Models on Edge Devices
Today, I’m extremely happy to announce Amazon SageMaker Edge Manager, a new capability of Amazon SageMaker that makes it easier to optimize, secure, monitor, and maintain machine learning models on a fleet of edge devices. Edge computing is certainly one of the most exciting developments in information technology. Indeed, thanks to continued advances in compute, […]
New – Amazon SageMaker Clarify Detects Bias and Increases the Transparency of Machine Learning Models
Today, I’m extremely happy to announce Amazon SageMaker Clarify, a new capability of Amazon SageMaker that helps customers detect bias in machine learning (ML) models, and increase transparency by helping explain model behavior to stakeholders and customers. As ML models are built by training algorithms that learn statistical patterns present in datasets, several questions immediately […]
New – Profile Your Machine Learning Training Jobs With Amazon SageMaker Debugger
Today, I’m extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage. Despite its impressive performance on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right […]
New – Data Parallelism Library in Amazon SageMaker Simplifies Training on Large Datasets
Today, I’m particularly happy to announce that Amazon SageMaker now supports a new data parallelism library that makes it easier to train models on datasets that may be as large as hundreds or thousands of gigabytes. As data sets and models grow larger and more sophisticated, machine learning (ML) practitioners working on large distributed training […]
Amazon SageMaker Simplifies Training Deep Learning Models With Billions of Parameters
Today, I’m extremely happy to announce that Amazon SageMaker simplifies the training of very large deep learning models that were previously difficult to train due to hardware limitations. In the last 10 years, a subset of machine learning named deep learning (DL) has taken the world by storm. Based on neural networks, DL algorithms have […]
In the Works – AWS Region in Melbourne, Australia
We launched new AWS Regions in Italy and South Africa in 2020, and are working on regions in Indonesia, Japan, Spain, India, and Switzerland. Melbourne, Australia in 2020 Today I am happy to announce that the Asia Pacific (Melbourne) region is in the works, and will open in the second half of 2022 with three […]
re:Invent 2020 Liveblog: Machine Learning Keynote
AWS Chief Evangelist Jeff Barr and Developer Advocates Martin Beeby and Steve Roberts liveblogged the first-ever Machine Learning Keynote. Swami Sivasubramanian, VP of Amazon Machine Learning shared the latest developments and launches in Amazon ML/AI, as well as demos of new technology, and insights from customers. Read the Machine Learning Keynote recap below. You can […]
Using Amazon CloudWatch Lambda Insights to Improve Operational Visibility
To balance costs, while at the same time ensuring the service levels needed to meet business requirements are met, some customers elect to continuously monitor and optimize their AWS Lambda functions. They collect and analyze metrics and logs to monitor performance, and to isolate errors for troubleshooting purposes. Additionally, they also seek to right-size function […]