MLOps: Simple Kubernetes with Skipper

Kubernetes can be very complex, this is why we created Skipper. Simple and flexible MLOps workflow engine, it helps to run ML (and not only) workloads on Kubernetes. Skipper comes with all the scripts to setup and run microservices infrastructure on Kubernetes. Try it and enjoy!

 

Python Numpy Array in ML Services

It can be not obvious how to pass Numpy array across separate services when running ML infra in separate containers. When data preparation service runs in a different container than training service. In this tutorial, I show how to convert Numpy to list and JSON to be able to send it through RabbitMQ message broker and consume it on the receiver side.

 

BIY Workflow with FastAPI, Python and Skipper

Build It Yourself. In this video, I explain how you can build workflow running with FastAPI REST generic endpoints. The best thing about it, Skipper architecture is modular and workflow runs in a separate Docker container. This means you can replace it with your own implementation if needed. I explain how workflow call is integrated into FastAPI logic and how the call is made to get the queue name from the workflow. Using this queue name and RabbitMQ message broker, event-based communication runs between containers.

 

MLOps: Extend Skipper ML Services

The goal of this video is to explain Skipper from MLOps user perspective, different blocks of Skipper and how they fit together. I show how a sample set of ML services works and how you could replace it or add your own service. Skipper engine is implemented with Python, but you could add service container implemented in any language. All runs on Kubernetes.

 

Running Kubernetes on Oracle Cloud OCI

Oracle Cloud OCI provides a good environment to run your Kubernetes workloads. In this video, I show how to access Kubernetes cluster in OCI, explain artifacts related to the cluster. I show how Skipper API runs on Kubernetes deployed on OCI. Cluster runtime is accessed through cloud shell.