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Combining Cloud and Microservices to Deploy Machine Learning Models


In one of our past blogs, we discussed the importance of the cloud and its many benefits, including better security, more storage, increased collaboration, cost effectiveness, and redundancy. Because the cloud has been a topic of much discussion in the past few years, most people understand these benefits of utilizing the cloud in their organization. Now we want to dive in a little deeper to determine why the cloud is so important specifically for deploying machine learning models and analytics.

If you are unfamiliar with the importance of the cloud, check out our blog post, or additional sites for more information:

Open Data Group



As companies go through their digital transformation and journey to the cloud, a critical part of the process involves creating applications based on microservices. A microservices based architecture has many benefits, including its ability to fit each organization’s specific needs and isolate system failures to certain components. When you build a microservice leveraging Docker, you can quickly migrate to the cloud  or hybrid architectures and utilize the applications that have been built.

The combination of cloud, microservices and analytics is a new and important trend that many organizations are implementing. This combination of technologies is so important because it allows the process of deploying machine learning models into production to be much more efficient, secure, and customized to your organization. Here at Open Data Group, our machine learning deployment technology, FastScore, leverages the cloud and microservices based architecture to deploy your machine learning models smarter than ever before. To learn more about FastScore, visit our product page here.


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