Glen Knight

NYC Based IT Professional

And Now a Word from Our AWS Heroes…

Whew! Now that AWS re:Invent 2018 has wrapped up, the AWS Blog Team is taking some time to relax, recharge, and to prepare for 2019. In order to wrap up the year in style, we have asked several of the AWS Heroes to write guest blog posts on an AWS-related topic of their choice. You […]

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Learn about New AWS re:Invent Launches – December AWS Online Tech Talks

Join us in the next couple weeks to learn about some of the new service and feature launches from re:Invent 2018. Learn about features and benefits, watch live demos and ask questions! We’ll have AWS experts online to answer any questions you may have. Register today! Note – All sessions are free and in Pacific […]

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New – EC2 P3dn GPU Instances with 100 Gbps Networking & Local NVMe Storage for Faster Machine Learning + P3 Price Reduction

Late last year I told you about Amazon EC2 P3 instances and also spent some time discussing the concept of the Tensor Core, a specialized compute unit that is designed to accelerate machine learning training and inferencing for large, deep neural networks. Our customers love P3 instances and are using them to run a wide […]

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New – AWS Well-Architected Tool – Review Workloads Against Best Practices

Back in 2015 we launched the AWS Well-Architected Framework and I asked Are You Well-Architected? The framework includes five pillars that encapsulate a set of core strategies and best practices for architecting systems in the cloud: Operational Excellence – Running and managing systems to deliver business value. Security – Protecting information and systems. Reliability – […]

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New for AWS Lambda – Use Any Programming Language and Share Common Components

I remember the excitement when AWS Lambda was announced in 2014! Four years on, customers are using Lambda functions for many different use cases. For example, iRobot is using AWS Lambda to provide compute services for their Roomba robotic vacuum cleaners, Fannie Mae to run Monte Carlo simulations for millions of mortgages, Bustle to serve billions of requests for their digital content. Today, […]

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New – Compute, Database, Messaging, Analytics, and Machine Learning Integration for AWS Step Functions

AWS Step Functions is a fully managed workflow service for application developers. You can think & work at a high level, connecting and coordinating activities in a reliable and repeatable way, while keeping your business logic separate from your workflow logic. After you design and test your workflows (which we call state machines), you can […]

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New – AWS Toolkits for PyCharm, IntelliJ (Preview), and Visual Studio Code (Preview)

Software developers have their own preferred tools. Some use powerful editors, others Integrated Development Environments (IDEs) that are tailored for specific languages and platforms. In 2014 I created my first AWS Lambda function using the editor in the Lambda console. Now, you can choose from a rich set of tools to build and deploy serverless applications. […]

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AWS Cloud Map: Easily create and maintain custom maps of your applications

Companies are increasingly building their applications as microservices (many separate services that each do a single job). Microservices often allow companies to iterate and deploy more quickly. Many of these microservice-based modern applications are built using various types of cloud resources and deployed on dynamically changing infrastructure. Previously you had to use configuration files to […]

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AWS Launches, Previews, and Pre-Announcements at re:Invent 2018 – Andy Jassy Keynote

As promised in Welcome to AWS re:Invent 2018, here’s a summary of the launches, previews, and pre-announcements from Andy Jassy’s keynote. I have included links to allow you to sign up for previews, as appropriate. (photo from AWS Community Hero Eric Hammond) Launches Here are the blog posts that we wrote for today’s launches: Amazon […]

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Amazon SageMaker Neo – Train Your Machine Learning Models Once, Run Them Anywhere

Machine learning (ML) is split in two distinct phases: training and inference. Training deals with building the model, i.e. running a ML algorithm on a dataset in order to identify meaningful patterns. This often requires large amounts of storage and computing power, making the cloud a natural place to train ML jobs with services such […]

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