Glen Knight

NYC Based IT Professional

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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Amazon Forecast – Time Series Forecasting Made Easy

The capacity to foresee the future would be an incredible superpower. At AWS, we can’t give you that, but we can help you use machine learning to forecast time series in a few steps. The goal of time series forecasting is to predict future values of time-dependent data such as weekly sales, daily inventory levels, […]

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Amazon Personalize – Real-Time Personalization and Recommendation for Everyone

Machine learning definitely offers a wide range of exciting topics to work on, but there’s nothing quite like personalization and recommendation. At first glance, matching users to items that they may like sounds like a simple problem. However, the task of developing an efficient recommender system is challenging. Years ago, Netflix even ran a movie […]

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AWS DeepRacer – Go Hands-On with Reinforcement Learning at re:Invent

Reinforcement Learning is a type of machine learning that works when an “agent” is allowed to act on a trial-and-error basis within an interactive environment, using feedback from those actions to learn over time in order to reach a predetermined goal or to maximize some type of score or reward. This stands in contrast to […]

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