We've actually used a Swagger API definition to create a REST API model that then declaratively generates the Python framework in order for us to serve that API. We just had to put in a very small amount of code in order to get it running. Then, we created a Docker container that captures not only our running code that is our service, but our pre-trained machine learning model, which then forms a package so that we are able to deploy and use our container. Finally, we used this container in order to serve and make predictions.
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You're reading from Hands-On Deep Learning for Images with TensorFlow
Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America). https://www.linkedin.com/in/will-ballard-b09115/
Read more about Will Ballard
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Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America). https://www.linkedin.com/in/will-ballard-b09115/
Read more about Will Ballard