Internet of Things (IOT), Wearable, Machine to Machine Learning, Sensor and Data Technologies

When I am delivering an introduction to the AWS Cloud for developers, I usually spend a bit of time to mention and to demonstrate Amazon Lightsail . It is by far the easiest way to get started on AWS. It allows you to get your application running on your own virtual server in a matter of minutes. Today, we are adding the possibility to deploy your container-based workloads on Amazon Lightsail . You can now deploy your container images to the cloud with the same simplicity and the same bundled pricing Amazon Lightsail provides for your virtual servers. Amazon Lightsail is an easy-to-use cloud service that offers you everything needed to deploy an application or website, for a cost effective and easy to understand monthly plan. It is ideal to deploy simple workloads, websites, or to get started with AWS. The typical Lightsail customers range from developers to small businesses or startups who are looking to get quickly started in the cloud and AWS. At any time, you can later adopt th...

The AWS Heroes program recognizes individuals from around the world who have extensive AWS knowledge and go above and beyond to share their expertise with others. The program continues to grow, to better recognize the most influential community leaders across a variety of technical disciplines. Introducing AWS DevTools Heroes Today we are introducing AWS DevTools Heroes: passionate advocates of the developer experience on AWS and the tools that enable that experience. DevTools Heroes excel at sharing their knowledge and building community through open source contributions, blogging, speaking, community organizing, and social media. Through their feedback, content, and contributions DevTools Heroes help shape the AWS developer experience and evolve the AWS DevTools, such as the AWS Cloud Development Kit, the AWS SDKs, and AWS Code suite of services. The first cohort of AWS DevTools Heroes include: Bhuvaneswari Subramani – Bengaluru, India DevTools Hero Bhuvaneswari Subramani is D...

Today, we are announcing that the Amazon Alexa team has migrated the vast majority of their GPU-based machine learning inference workloads to Amazon Elastic Compute Cloud (EC2) Inf1 instances, powered by AWS Inferentia . This resulted in 25% lower end-to-end latency, and 30% lower cost compared to GPU-based instances for Alexa’s text-to-speech workloads. The lower latency allows Alexa engineers to innovate with more complex algorithms and to improve the overall Alexa experience for our customers. AWS built AWS Inferentia chips from the ground up to provide the lowest-cost machine learning (ML) inference in the cloud. They power the Inf1 instances that we launched at AWS re:Invent 2019 . Inf1 instances provide up to 30% higher throughput and up to 45% lower cost per inference compared to GPU-based G4 instances, which were, before Inf1, the lowest-cost instances in the cloud for ML inference. Alexa is Amazon’s cloud-based voice service that powers Amazon Echo devices and more th...

To be able to run analytics, build reports, or apply machine learning, you need to be sure the data you’re using is clean and in the right format. That’s the data preparation step that requires data analysts and data scientists to write custom code and do many manual activities. First, you need to look at the data, understand which possible values are present, and build some simple visualizations to understand if there are correlations between the columns. Then, you need to check for strange values outside of what you’re expecting, such as weather temperature above 200℉ (93℃) or speed of a truck above 200 mph (322 km/h), or for data that is missing. Many algorithms need values to be rescaled to a specific range, for example between 0 and 1 , or normalized around the mean . Text fields need to be set to a standard format, and may require advanced transformations such as stemming . That’s a lot of work. For this reason, I am happy to announce that today AWS Glue DataBrew is available,...