Bring your AI experiment to production with the lambda architecture and continuous delivery
Running AI experiments on your own machine can be fun. But production is the place to be. But how do you experiment with AI in production? In this session I will introduce an architecture and methodology based on continuous delivery and the lambda architecture to run AI experiments in production.
I believe that it is vital that you run AI experiments in production to make the most of AI. After all that is where the users of your system are. By applying continuous delivery and the lambda architecture you create the right environments to run AI experiments in production.
The use of the combination of continuous delivery and lambda architecture enables developers to quickly deploy changes to their AI solution. It also enables business users to help data scientists improve the AI models by providing vital feedback on real data.
In this session I will introduce an architecture and methodology based on continuous delivery and the lambda architecture to run AI experiments in production. At the end of the session people will have a solid understanding of running AI in production and how to maximize the impact of their efforts in this regard.
The session gives answers to the following questions:
– Why do you want to experiment with AI in production?
– What is the lambda architecture and how does it relate to continuous delivery?
– How can the lambda architecture help you in allowing non-developers train your AI models?
– In what ways can you use continuous delivery to quickly improve your AI solution?
– What tools can you use to deploy a lambda architecture with continuous delivery?