More and more real-world systems are relying on data science and analytical models to deliver sophisticated functionality or improved user experiences. For example, Microsoft combined the power of advanced predictive models and web services to develop the real-time voice translation feature in Skype. Facebook and Google continuously improve their deep learning models for better face recognition features in their photo service.
Some have characterised this trend as a shift from Software-as-a-Service (SaaS) to an era of Models-as-a-Service (MaaS). These models are often written in statistical programming languages (e.g., R, Python), which are especially well suited to analytical tasks.
With analytical models playing an increasingly important role in real-world systems, and with more models being developed in R and Python, we need powerful ways of turning these models into APIs for others to consume.
But how?
It is actually a lot easier than you might think. I wrote a step-by-step guide about deploying analytical models as REST APIs. This guide will walk you through how to set up your own MaaS WITHOUT a team of full-stack developers/engineers. All you need are the R/Python models you develop and a Domino Data Lab account. You can find the full article on ProgrammableWeb here.
You can also find my slides for the related LondonR talk here:
When I created this blog back in 2013, my aim was simply to learn ggplot2. Thanks to the feedback and advice from the R community, I continued to learn new stuff and somehow found an opportunity to work for Domino and Virgin Media. I wouldn't say I have seen enough to make a fair comparison with other programming communities. But so far the support from the R community, for me, has been truly special! I believe blogging is one of the best ways to contribute so I better get back to the writing habit! For the next post, I would like to talk about using R with other Microsoft tools (SQL Server, PowerPoint) in a commercial environment.
I met Alex Glaser and Wojtek Kostelecki after my LondonR talk. They have already set up a meetup for Kagglers. We are working on a collaborative project to build / test / stack models on the Domino platform. For more information, join the meetup first. Let's Kaggle together!
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This is a crucial topic for making data science models more accessible and actionable! Turning R or Python models into APIs can significantly enhance their usability and integration into broader applications. Your guide on this process is incredibly timely and useful, especially for those looking to deploy their models in production environments.
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This is an excellent guide for making your models more accessible and deployable! Transforming R or Python models into APIs is a game-changer for integrating machine learning into applications and services. Your step-by-step approach simplifies what can be a complex process, making it much more approachable.
ReplyDeleteI’m interested to know—what are some common pitfalls or challenges you’ve encountered when creating APIs for models? Also, do you have any recommendations for best practices to ensure the APIs are robust and scalable?
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