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Acupressure & Allied Healing by Mr. Satish

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EBook €? Free EBook: A Developer’s Guide To Building AI Applications



This diagram illustrates how the APIs you build in Amazon API Gateway provide you or your developer customers with an integrated and consistent developer experience for building AWS serverless applications. API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls. These tasks include traffic management, authorization and access control, monitoring, and API version management.




eBook – Free eBook: A Developer’s Guide to Building AI Applications



Best practices for deploying containers (Level 200) Building applications with containers has become the preferred approach for developers. Twelve-Factor App, a methodology for building software-as-a-service apps has become the most popular and adopted methodology to design and architect applications in the cloud. In this session, we deep dive into how you can implement each factor in the Twelve-Factor App methodology with AWS Copilot to run and operate scalable, reliable, secure, and extensible apps with Amazon ECS. We also tackle how to overcome the lengthy process to run apps in the cloud and make it a non-trivial challenge to get the apps to the market, even if an app is running in localhost.


A developer's guide to modern application development (Level 200) In this session, learn how to improve your workflow when building applications in the cloud. You can use AWS Toolkit within your IDE of choice to access your AWS environment, as well as Amazon CodeWhisperer which provides ML powered coding recommendations when developing your applications. In addition, when submitting your code, Amazon CodeGuru can provide both security and performance analysis. Depending on what you are building, AWS Amplify can make frontend development a breeze and AWS SAM makes serverless easy. To conclude, learn how to use AWS CDK for cloud infrastructure and AWS Copilot for backend applications.


Finding the right hardware solution is a vital building block to creating an AI system that performs efficiently and effectively. By focusing on machine learning inference, Xilinx machine learning tools can help software developers deploy machine learning applications for real-time inference with support for many common machine learning frameworks, including TensorFlow, Pytorch and Caffe, as well as Python and RESTful APIs.


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