AWS News Blog
New – VPC Ingress Routing – Simplifying Integration of Third-Party Appliances
When I was delivering the Architecting on AWS class, customers often asked me how to configure an Amazon Virtual Private Cloud to enforce the same network security policies in the cloud as they have on-premises. For example, to scan all ingress traffic with an Intrusion Detection System (IDS) appliance or to use the same firewall […]
Amazon EC2 Update – Inf1 Instances with AWS Inferentia Chips for High Performance Cost-Effective Inferencing
Our customers are taking to Machine Learning in a big way. They are running many different types of workloads, including object detection, speech recognition, natural language processing, personalization, and fraud detection. When running on large-scale production workloads, it is essential that they can perform inferencing as quickly and as cost-effectively as possible. According to what […]
AWS Outposts Now Available – Order Yours Today!
We first discussed AWS Outposts at re:Invent 2018. Today, I am happy to announce that we are ready to take orders and install Outposts racks in your data center or colo facility. Why Outposts? This new and unique AWS offering is a comprehensive, single-vendor compute & storage solution that is designed to meet the needs […]
AWS Now Available from a Local Zone in Los Angeles
AWS customers are always asking for more features, more bandwidth, more compute power, and more memory, while also asking for lower latency and lower prices. We do our best to meet these competing demands: we launch new EC2 instance types, EBS volume types, and S3 storage classes at a rapid pace, and we also reduce […]
Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning
Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML). We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, […]
Amazon SageMaker Debugger – Debug Your Machine Learning Models
Today, we’re extremely happy to announce Amazon SageMaker Debugger, a new capability of Amazon SageMaker that automatically identifies complex issues developing in machine learning (ML) training jobs. Building and training ML models is a mix of science and craft (some would even say witchcraft). From collecting and preparing data sets to experimenting with different algorithms […]
Amazon SageMaker Model Monitor – Fully Managed Automatic Monitoring For Your Machine Learning Models
Today, we’re extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear. The first thing I learned when I started working with data is that there is no such thing as paying too much […]
Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation
Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure. Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.: Converting […]