Building search systems is hard. Preparing them to work with machine learning is really hard. Developing a complete search engine framework integrated with AI is really really hard. In this post, we'll build a search engine from scratch and discuss on how to further optimize results by adding a machine learning layer using Kubeflow and Katib. This new layer will be capable of retrieving results considering the context of users and is the main focus of this article. As we'll see, thanks to Kubeflow and Katib, final result is rather quite simple, efficient and easy to maintain. To understand the concepts in practice, we'll implement the system with hands-on experience. As it's been built on top of Kubernetes, you can use any infrastructure you like (given appropriate adaptations).

Machine learning must address a daunting breadth of functionalities around building, training, serving, and managing models. Doing so in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying machine learning workloads. The Kubeflow project's development has been a journey to realize this promise, and we are excited that journey has reached its first major destination – Kubeflow 1.0. Always ready to work with a strong and diverse community, IBM joined this Kubeflow journey early on.