Product Recommendation system on Kubernetes using Argo Workflows
summary
Builds an end-to-end product recommendation pipeline that runs on Kubernetes using Argo Workflows, packaging each step as a container image pushed to Docker Hub and defined in YAML templates; pipeline implements a DAG with preprocess → train → register tasks, pulls images, runs Spark jobs on Kubernetes and stores blobs in Minio, deployed on an Azure VM (Minikube) for the demo. The workflow executes successfully in the dashboard, running preprocessing, model training, and model registration containers.
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chapters (6)
- 0:00Start
- 7:41Recommender Systems
- 8:40You get recommendations based on name and product image similarity
- 9:53Training and Serving is Hard at Scale
- 11:39But for Product Recommendation, it's about machine learning pipelines
- 14:35And how'd we use Argo Workflows?
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