This best-practices article intends for developers interested in creating RESTful Web services that provide high reliability and consistency across multiple service suites; following these guidelines; services are positioned for rapid, widespread, public adoption by internal and external clients.
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The challenge of establishing reference architectures for large-scale machine learning solutions is accentuated by two main factors:
Machine learning frameworks and infrastructure have evolved considerably faster than the adoption of those technologies in mainstream environments.
The lifecycle of machine learning solutions is fundamentally different from other software disciplines.
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Build and Deploy Data Science Products : A Practical Guide to Building a Machine Translation Application This journey is going to be a 8 steps.
In this series we will take a use case, understand the solution landscape and its evolution, explore different architecture choices, look under the hood of the architecture to understand the nuts and bolts, build a prototype, convert the prototype into production ready code, build an application from the production ready code and finally understand the process for deploying the application .
