Simplifying MLOps with Kubeflow (Metadata and Artifacts) in GCP
Explore MLOps concepts with Kubeflow and Google Cloud's Vertex AI Pipelines! Learn how to manage models, track experiments, and handle datasets efficiently. This tutorial covers:
Setting up Vertex AI Workbench
Creating pipeline components
Compiling and running ML pipelines
Working with metadata and artifacts
Deploying models to endpoints
Troubleshooting common issues
Perfect for data scientists and ML engineers looking to streamline their workflow. Dive into the world of MLOps and discover how to build scalable, reproducible machine learning pipelines on Google Cloud Platform.
Demonstration Code and Diagram: https://github.com/nodematiclabs/vert...
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0:00 Conceptual Overview
0:39 Vertex AI Workbench (Jupyter)
1:53 Python Notebook
4:17 Dataframe (Dataset) Code
5:23 Scikit-Learn Training Code
6:22 Vertex AI Endpoints Code
6:45 Kubeflow Pipeline Code
8:09 Vertex AI Pipelines Runs
20:12 Endpoint Serving Containers
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