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blog.keras.io | ||
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yos.io
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| | | | | You're building a REST API. You develop an API backend with a few endpoints and deploy it to production. You publish several official language-specific API clients as well as an API documentation. Your API announces its public release; other developers start using it. Your day ends on a happy note. The following day, you want to add a new feature to your API. You notice that you have to do a few things: Update the server implementation to support the new feature. Update the client libraries (one for each supported platform and language.) Update the documentation. All the above must be consistent with each other. You let out a heavy sigh. | |
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pyimagesearch.com
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| | | | | In today's tutorial you'll learn how to build a scalable deep learning REST API using Keras, Flask, Redis, and message queuing/message brokers. | |
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pyimagesearch.com
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| | | | | This tutorial details how to create your own face detection API using Python, OpenCV, and Django. You can detect faces in the cloud with this simple API. | |
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brunoscheufler.com
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| | | When you build a service focused around a GraphQL endpoint, you might think of testing strategies that can make use of it. After all, your resolvers will contain large parts of the actual business logic, otherwise, exposing an API wouldn't be useful, would it?... | ||
