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Predicting Customer Satisfaction Using ZenML, MLFlow & Streamlit
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2,949Views
2022Apr 26
In this video, we use ZenML to build a production-ready pipeline to predict the customer satisfaction score for the next order or purchase. We will be using the Brazilian E-Commerce Public Dataset by Olist. At the end, we set up a continuous training and deployment pipeline that updates a deployed Streamlit data application with the latest trained model. We track all our pipelines using MLFlow. ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. // Deployed Streamlit application https://share.streamlit.io/ayush714/c... // Blog post https://blog.zenml.io/customer_satisf... // Code Repository https://github.com/zenml-io/zenfiles/... // ZenML Related Resources ZenML Website: https://zenml.io ZenML Docs: https://docs.zenml.io ZenML GitHub: https://github.com/zenml-io/zenml

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ZenML

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