Excel Compute Fabric

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Highly-Scalable Compute Fabric for Excel TM ,[object Object]

Compute Fabric For Excel TM Architecture Overview 13/10/09 © Velocimetrics Ltd Slide

Compute Fabric For Excel TM Concepts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],13/10/09 © Velocimetrics Ltd Slide

Compute Fabric For Excel TM Orchestration Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],13/10/09 © Velocimetrics Ltd Slide

Compute Fabric For Excel TM Resilience &Scalability ,[object Object],[object Object],[object Object],[object Object],[object Object],13/10/09 © Velocimetrics Ltd Slide

Inside the Excel Worker 13/10/09 © Velocimetrics Ltd Slide

Questions? 13/10/09 © Velocimetrics Ltd Slide

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Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects. In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected. Note: This workshop focuses on containerizing MXNet. The features of MXNet and capabilities of deep learning in general are vast, and there are recorded sessions from re:Invent that dive deeper on these topics. All you need to participate is a laptop and AWS account. Pizza will be provided.

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by Asif Khan, Technical Business Development Manager, AWS Deep learning is an implementation of machine learning that uses neural networks to solve difficult and complex problems, such as computer vision, natural language processing, and recommendations. Due to the availability of deep learning libraries and frameworks, developers have the ability to enhance the capabilities of their applications and projects. In this workshop, you learn how to build and deploy a powerful deep learning framework called MXNet on containers. The portability and resource management benefit of containers means developers can focus less on infrastructure and more on building. The labs start by demonstrating the automation capabilities of AWS CloudFormation to stand up core infrastructure; as an added bonus, you use Spot Fleet to leverage the cost benefits of using Spot Instances, especially for developer environments. Then, you walk through creating an MXNet container in Docker and deploying it with Amazon ECS. Finally, you walk through an image classification demo of MXNet to validate that everything is working as expected. Note: This workshop focuses on containerizing MXNet. The features of MXNet and capabilities of deep learning in general are vast, and there are recorded sessions from re:Invent that dive deeper on these topics. All you need to participate is a laptop and AWS account. Pizza will be provided. Level 300

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Presented in this document is a description of how to develop and deploy industrial applications in a timely fashion using Excel/VBA as the user-interface (UI) and systems-integration (SI) system, IMPL as the industrial modeller and CPLEX as the commercial solver. A small jobshop scheduling example is overviewed to help describe to some extent, the details of this advanced decision-making application where this type of problem can be found in both the manufacturing and process industries. The purpose of developing and deploying quickly is to acquire feedback from the end-users, to assess the difficulty and tractability of the problem, to ascertain the expected costs and benefits of the application and to address any other issues and requirements regarding the project as a whole as soon as possible. For some projects, proof-of-concepts, prototypes and/or pilots are also useful and these should also be performed ASAP as well using the same approach highlighted here. Ultimately, once a business problem solution has been achieved and full or partial benefits have been captured, then a more robust and sophisticated end-user experience and system architecture can be implemented in the operating system and computer programming environment of choice which will hopefully enhance and maintain the solution over its expected life-cycle.

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