Nowadays Artificial Intelligence (AI) and Machine Learning (ML) are part of our daily lives thanks to a multitude of applications: facial recognition, digital assistants, automatically generated subtitles …
One of the main challenges faced by companies that 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. 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.
And it is in this context that a new generation of tools and services in the Cloud appears to bridge the gap between these two cultures, and automate end-to-end the pipeline of models and data.
In this talk we will talk about some of these tools and show examples of how to incorporate them into different cloud solutions from a DevOps point of view.