Deploy and maintain ML models in production. MLflow tracking, model serving, monitoring drift, and automated retraining.
Student builds a simple ML model and deploys it using MLflow, demonstrating understanding of model deployment basics.
Student builds a data versioning system using DVC for an image classification model, showcasing data management skills.
Student creates a Kubeflow pipeline for a text classification model, demonstrating ability to automate ML workflows.
Student deploys a regression model using Seldon Core, proving understanding of model serving concepts.
Student sets up Prometheus to monitor an ML model's performance, demonstrating ability to track model metrics.
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