MLOps with TensorFlow and Kubernetes Powered by RabbitMQ and FastAPI

I show how to run TensorFlow model training and data processing containers in a single Pod in Kubernetes. Model training container runs as a main container and data processing as a sidecar. Running both containers in a single Pod, allow to share files in common storage. I'm using persistence volume to store TensorFlow model and stats data from the data processing container. This video shows a walk through the complete use case of data processing, model training and ML microservices communication.

 

RabbitMQ on Kubernetes in Skipper

RabbitMQ works great for event-based microservices implementation. Katana ML Skipper is our open source product, it helps to run workflows and connect multiple services, we are specifically specialized for ML workflows. In this video, I explain how we integrated RabbitMQ and how we run it on Kubernetes cluster. I believe this can be helpful if you are researching how to run RabbitMQ on Kubernetes cluster for your own use cases.

 

FastAPI on Kubernetes with NGINX Ingress

A simple tutorial about a complex thing - how to expose FastAPI app to the world on Kubernets with NGINX Ingress Controller. I explain the structure of Kubernetes Pod for FastAPI along with Kubernetes service. I show how FastAPI properties should be set to be accessible through Ingress path definition. You will learn how to check the log for NGINX Ingress Controller and FastAPI Pod.