Wireless communication has put us closer to each other. 5G networks increase the reach of what we can achieve to new use cases that need end-to-end low latency. With AWS Wavelength, you can deploy AWS compute and storage services within telecommunications providers’ data centers at the edge of the 5G networks. Your applications can then deliver single-digit millisecond latencies to mobile devices and end users and, at the same time, seamlessly access AWS services in the closest AWS Region. For example, low latency enables new use cases such as: Delivery of high-resolution and high-fidelity live video streaming. Improved experience for augmented/virtual reality (AR/VR) applications. Running machine learning (ML) inference at the edge for applications in medical diagnostics, retail, and factories.…
Month: April 2022
AWS Week in Review – April 25, 2022
This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! The first in this year’s series of AWS Summits took place in San Francisco this past week and we had a bunch of great announcements. Let’s take a closer look… Last Week’s Launches Here are some launches that caught my eye this week: AWS Migration Hub Orchestrator – Building on AWS Migration Hub (launched in 2017), this service helps you to reduce migration costs by automating manual tasks, managing dependencies between tools, and providing better visibility into the migration progress. It makes use of workflow templates that you can modify and extend, and includes a…
Amazon SageMaker Serverless Inference – Machine Learning Inference without Worrying about Servers
In December 2021, we introduced Amazon SageMaker Serverless Inference (in preview) as a new option in Amazon SageMaker to deploy machine learning (ML) models for inference without having to configure or manage the underlying infrastructure. Today, I’m happy to announce that Amazon SageMaker Serverless Inference is now generally available (GA). Different ML inference use cases pose different requirements on your model hosting infrastructure. If you work on use cases such as ad serving, fraud detection, or personalized product recommendations, you are most likely looking for API-based, online inference with response times as low as a few milliseconds. If you work with large ML models, such as in computer vision (CV) applications, you might require infrastructure that is optimized to run…
Amazon Aurora Serverless v2 is Generally Available: Instant Scaling for Demanding Workloads
Today we are very excited to announce that Amazon Aurora Serverless v2 is generally available for both Aurora PostgreSQL and MySQL. Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora that allows your database to scale capacity up or down based on your application’s needs. Amazon Aurora is a MySQL- and PostgreSQL-compatible relational database built for the cloud. It is fully managed by Amazon Relational Database Service (RDS), which automates time-consuming administrative tasks, such as hardware provisioning, database setup, patches, and backups. One of the key features of Amazon Aurora is the separation of compute and storage. As a result, they scale independently. Amazon Aurora storage automatically scales as the amount of data in your database increases. For example,…