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In this project, I developed a completed Vertex and Kubeflow pipelines SDK to build and deploy an AutoML / BigQuery ML regression model for online predictions. Using this ML Pipeline, I was able to develop, deploy, and manage the production ML lifecycle efficiently and reliably.

zacharyvunguyen/Production-Ready-ML-Pipeline-on-GCP-Baby-Weight-Prediction

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Production-Ready ML Pipeline on GCP: Baby Weight Prediction

In this project, I developed a completed Vertex and Kubeflow pipelines SDK to build and deploy an AutoML / BigQuery ML regression model for online predictions. Using this ML Pipeline, I was able to develop, deploy, and manage the production ML lifecycle efficiently and reliably.

  • As part of this project, I used the Natality dataset, a public dataset available in BigQuery that provides information on US births from 1969 to 2008.

  • The trained AutoML/BQML models predicted the weight of newborns. The predicted values would be used in order to provide care for the newborns.

  • At the end, a streamlit application is then created to show how the model can interact with a web application to provide online predictions.

Streamlit App for Online Predictions

Vertex AI Pipeline Structure


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In this project, I developed a completed Vertex and Kubeflow pipelines SDK to build and deploy an AutoML / BigQuery ML regression model for online predictions. Using this ML Pipeline, I was able to develop, deploy, and manage the production ML lifecycle efficiently and reliably.

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