Machine Learning Deployment: 3 Model Deployment Paradigms, Monitoring and Alerting

In this hands-on course, data scientists and data engineers learn best practices for deploying machine learning models in these paradigms: batch, streaming, and real time using REST. It explores common production issues faced when deploying machine learning solutions and monitoring these models once they have been deployed into production. By the end of this course, you will have built the infrastructure to deploy and monitor machine learning models in various deployment scenarios. This course is taught entirely in Python and pairs well with the MLflow course.


 
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