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...
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...
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...
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...
Tens of thousands of customers use Amazon EMR to run big data analytics applications on frameworks such as Apache Spark, Hive, HBase, Flink, Hudi, and Presto at scale. EMR automates the provisioning and scaling of these frameworks and optimizes performance with a wide range of EC2 instance types...
We are excited to announce Amazon Lookout for Metrics, a new service that uses machine learning (ML) to detect anomalies in your metrics, helping you proactively monitor the health of your business, diagnose issues, and find opportunities quickly – with no ML experience required.
Lookout for Metrics uses the same...
