CodeBuild is the software equivalent to a real-world conveyor belt. With CodeBuild you can compile your software source code, run tests on your code, and produce software packages for deployment to customers. This is done in a fully managed, secure, and cost optimized environment.
Month: May 2019
Once again it’s time for another Barney Style “Break Down!”. Translating complex IT (InformationTechnology) terms into simple and relatable terms. Today we are going to dive deeper into an AWS resource that makes DevOps (DevSecOps) agile and flexible. If you are not familiar with the Barney Style breakdown on DevOps follow this link tbs DevSecOps . This article is going to breakdown the features of a software product that Amazon offers.
It’s time to break IT (information technology) down “Barney Style!” With so many buzzwords in the computer world, it can be a mind numbing experience when a business owner hears a word like “DevSecOps.” Barney Style Solutions wants to draw the simplest connection from what you know as a business owner to what information technology enthusiasts know from being nerds. This post will follow the “techie-speech is to a normal-day-example” method, in which we will use techie buzzwords and draw connections to common business principles. So let’s do it. Let’s break it down Barney Style.
Speed and flexibility are important superpowers to have in today’s information technology world. Think about it from a traditional “brick-and-mortar” business scenario. When you produce a widget, you want to get it from the stages of imagination to production in the fastest and most sensible manner. Having a streamlined method of getting business ideas from conception to production is “make it or break it”. Two companies offering the same type of service to the same pool of customers will need the added benefit of testing their product to see how good the customer reception is. What processes are in place at your company to service your clients with products in the most efficient manner? DevOps has been a buzzword in the software and IT community for a while. What are the operations your company uses to develop software in a swift, flexible, and maintainable manner, so that you can competitively reach clients? Barney Style Solutions wants to give you our “Barney Style” Breakdown on AWS (Amazon Web Services) DevOps culture, principles, and practices. For more information on AWS DevOps click the link. This article briefly discusses the services and key features of AWS DevOps.
ML (Machine Learning) makes it possible to accomplish business goals by manipulating information collected from a server. SageMaker Neo is an Amazon Web Services machine learning(ML) resource that makes it easier to use artificial intelligence to super power your business production through smarter predictions. You’ll save time and still be accurate when using your ML training models. Neo is automatically optimized to give you double the operating power for use on any ML framework and target hardware platform. The open source documentation gives developers the ability to open the hood of Neo and customize their ML environment. Neo offers numerous benefits that can be used on any server.
If you want to read and write data on Facebook’s platform, then you have to use the Graph API. Because it is HTTP-based, you can use it to query data, post stories, manage ads, upload photos, and perform a multitude of other tasks. Creative developing will help you plan the best use of the API calls you make.
The Graph API V3.3 The premier method of reading and writing to the Facebook social graph for apps is the Graph API. If you want to know how to interact with all of Facebook’s SDKs and products, or use other APIs which are extensions of the Graph API, then you have understand the Graph API. …
Highly accurate training datasets built by machine learning that saves up to 70% of labeling costs AWS SageMaker Ground Truth is a resource for building high-quality training datasets for machine learning. You will be able to provide human and public labelers with built-in workflows and interfaces for common labeling tasks. Data is eventually labeled automatically, …
Amazon’s AWS SageMaker makes it possible to build, train and deploy machine learning models quickly. It is fully managed and covers the entire ML (machine learning) workflow. A lot of large companies use SageMaker resource. With SageMaker you can; collect and prepare training data, choose and optimize your ML algorithms, setup and manage training environments, and tune your model for optimization. Models can be deployed and managed in production. Use reinforcement learning to build smart outcomes. SageMaker is open and flexible and AWS will provide detailed instructions on how to use the SageMaker resource.