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Service Terms

In addition to the rights granted to AISPL Content under the Agreement, we also grant you a limited, non-exclusive, non-sublicensable (except to End Users as provided below), non-transferrable license to do the following during the Term: (a) Development: You may use, reproduce, modify, and create derivative works of the Lumberyard Materials to develop and support video games, software, audio-visual works, and other content (each work created through use of the Lumberyard Materials is a “Lumberyard Project”).

However, you may do so only if (i) the Lumberyard Project provides material content or functionality beyond that provided by the Lumberyard Redistributables themselves, (ii) the Lumberyard Redistributables are integrated into the Lumberyard Project so they are not separately usable by End Users, (iii) you do not distribute in source code form Lumberyard Redistributables that we or our affiliates make available in file formats that are commonly compiled (e.g., C, C++) or for which we or our affiliates make a compiler available, and (iv) you ensure End Users are subject to terms no less protective of the Lumberyard Materials than these Service Terms, including this Section and Sections 42.4 and 42.5 below.

You may reproduce and distribute (but not sublicense) the Lumberyard Materials (including any permitted modifications and derivatives): (i) to other AWS and AISPL customers that are contractors of yours solely for the purpose of allowing those AWS customers to perform work on your behalf, (ii) to other AWS and AISPL customers in connection with work you perform for them as a contractor, and (iii) to up to 5 other AWS and AISPL customers who you authorize to distribute a Lumberyard Project in connection with your sale or licensing of that Lumberyard Project (e.g., publishers of a game you develop).

Without limiting the license restrictions set out in the Agreement, you may not (a) distribute the Lumberyard Materials in source code form, except as expressly permitted by Section 42.2(b) and (c), (b) use or exploit the Lumberyard Materials or any portion thereof to develop, maintain, participate in the development of, or support any competing engine, development tool, or software framework, (c) use the Lumberyard Materials or any portion thereof as part of a logo or trademark, (d) remove, obscure, or alter any proprietary rights notices (including copyright and trademark notices) contained in the Lumberyard Materials, (e) take any action that would require us or our affiliates or you to license, distribute, or otherwise make available to anyone the Lumberyard Materials under different terms (e.g., combining Lumberyard Materials with software subject to “copyleft” open source licenses), or (f) use or exploit the Lumberyard Materials or any portion thereof in any manner or for any purpose other than as expressly permitted by these terms.

However, this restriction will not apply in the event of the occurrence (certified by the United States Centers for Disease Control or successor body) of a widespread viral infection transmitted via bites or contact with bodily fluids that causes human corpses to reanimate and seek to consume living human flesh, blood, brain or nerve tissue and is likely to result in the fall of organized civilization.

Google Cloud vs AWS: (2020 Comparison)

Amazon web service is a platform that offers flexible, reliable, scalable, easy-to-use, and cost-effective cloud computing solutions.

AWS cloud computing platform offers a massive collection of cloud services that build up a fully-fledged platform.

Here are the important pros/benefits of selecting AWS web services: Here are the pros/benefits of selecting Google cloud services: Important features of AWS are: Important features of Google Cloud are:

AIOps as a Service: Solving Problems on the Customer’s Terms

But whether it’s completing a task more efficiently, automating a formerly manual process, or unifying a distributed workforce, merely developing a new cloud application isn’t the end goal.

Technologies and services must work together to be available and reliable, and they must be consumable in a variety of ways so that companies that rely on the cloud can achieve their unique business missions no matter their underlying cloud strategy.

The power of the cloud is long-established, and whether an organization uses public, private, or hybrid cloud resources, they expect that the infrastructure, services, and support they need will be available to them as they require.

SaaS is a big part of that power, but beyond that, the cloud gives the customer the ability to adapt to changing needs as novel technologies are integrated into the IT environment, including: That ease of adoption and integration means today’s IT environments are highly complex.

That is because monitoring and managing a complex, cloud-forward technology environment requires a platform that is built for the cloud, and that has the speed and intelligence to keep pace with the demands associated with the cloud.

Artificial intelligence for IT Operations (AIOp’s) is making it possible to see the entire spectrum of the IT environment, whether in the public cloud, the customer’s cloud, or in a hybrid environment that may include public and private clouds, as well as on-premises technologies.

That intelligence, integral to AWS, means organizations can move workloads closer to the network edge, resulting in more responsive service delivery and improved performance in hybrid cloud environments.

Introduction to Amazon Translate

Over the next few minutes, I'll highlight the services features and benefits, talk about how it works and how you can get started using it, and walk through some of the popular use cases.

I've also included some demos throughout the course, so you can get a concrete look at how to connect to the Amazon Translate API and see some examples of applications built around the service.

Amazon Translate is a neural machine translation service powered by deep learning models that allow for fast and accurate translation supporting multiple languages.

It's a continually train solution that allows you to perform batch translations when you have large volumes of pre-existing text as well as real-time and on-demand translations when you want to deliver content as a feature of your application.

As an AWS service, Amazon Translate integrates nicely with several other AWS services such as Amazon Polly for translated speech-enabled products, Amazon Comprehend for analysis of translated text, and Amazon Transcribe for localized captioning of your media products.

If you're already an AWS customer and you're looking for a translation solution, it's convenient to stay within the AWS ecosystem for easier integration with other applications and for more efficient security of your data.

Amazon Translate, powered by a neural machine translation engine, offers increased accuracy of translation when compared to traditional statistical and rule-based translation models.

The encoder reads the source sentence one word at a time and constructs a semantic representation that captures the meaning of the source text.

Amazon Translate uses attention mechanisms to understand context and decide which of those words in the source are most relevant for generating the next target word.

One of the main advantages of the attention mechanism is to enable the decoder to shift focus on certain parts of the source sentence to make sure that ambiguous words or phrases are translated correctly.

The decoder uses the semantic representation and the attention mechanism to generate a translation one word at a time in the target language.

From within the service console, you can immediately start translating text, just choose the source and target language and then enter the text you want translated in the source language text box.

Here's an example in Python where we call the translate text operation and pass the source text along with the source language and target language.

Most use cases fall under one of two main categories, translating web-authored content for localization purposes, either on-demand or in real time, and batch translating pre-existing content for analysis and insights,.

So while there's a lot going on here in terms of translation, all of that was taken care of with just one line of Python code in a lambda function.

Unlike conventional phrase-based machine translation, Amazon Translate takes into account the entire context of the source sentence as well as the translation it has previously generated.

AWS to Azure services comparison

This article helps you understand how Microsoft Azure services compare to Amazon Web Services (AWS).

As the leading public cloud platforms, Azure and AWS each offer a broad and deep set of capabilities with global coverage.

Yet many organizations choose to use both platforms together for greater choice and flexibility, as well as to spread their risk and dependencies with a multicloud approach.

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