With Artificial Intelligence (AI) and Machine Learning (ML) as part of our daily lives, thanks to a multitude of applications: facial recognition, digital assistants and automatically generated subtitles, one of the main challenges faced by businesses which want to deploy AI and ML applications is to make two quite opposite worlds coexist. On the one hand, data scientists, experts in algorithms, mathematics and research methodology and on the other hand, DevOps, with its paradigm focused on the platform, automation, instrumentation and processes. These two worlds find it difficult to understand each other.

In this context, we share how a new generation of tools and services in the Cloud can bridge the gap between these two extremes, and automate the pipeline of models and data from end to end, providing a quick and easy way to get ML models from ideation to production.