A Software Engineer’s Journey into ML Deployment : Discovering Kubeflow
As a software engineer, I’ve always been fascinated by the ways cloud technology has evolved over the past decade to solve complex infrastructure issues. Recently, while working on a personal project with an ML module, I was exploring options for deploying my ML model. The first problem I faced was understanding why do we even need ML-specific solutions in the first place.
Why not just use Kubernetes for ML model deployment? Isn’t Kubernetes the all-in-one solution? Initially, I thought, “Why do we even need this abstraction when Kubernetes already exists?”
This scenario challenged my current cloud knowledge and pushed me to dive deeper into MLOps, a field I knew little about before. While exploring MLOps, I came across several cloud solutions such as TensorFlow Extended (TFX), AWS Neuron, and Google’s Vertex AI. However, the one whose architecture intrigued me the most was Kubeflow.
I chose Kubeflow because it resonated the most with my idea of an ML deployment solution. From what I understood, Kubeflow builds on top of existing Kubernetes components, optimizing them specifically for the ML lifecycle.
