Data Science Model Deployments and Cloud Computing on GCP - Introduction to ML Model Lifecycle

The video tutorial covers the lifecycle of a machine learning model, starting from ideation to deployment and operationalization. It emphasizes the importance of understanding the problem, data exploration, and preprocessing. The development phase involves selecting the right algorithm and framework, followed by model training and deployment. The tutorial also discusses ML OPS, focusing on choosing appropriate cloud services and budget considerations. Performance monitoring, including hyperparameter tuning and model versioning, is highlighted. The course will teach deploying a custom model using Scikit-learn on Google App Engine, validating input data, scheduling retraining workflows, and serving predictions with Flask.

Describe the process of performance monitoring for a deployed machine learning model.

What steps are involved in retraining and redeploying a machine learning model?