Integrating Agentic AI with Existing Machine Learning Pipelines

A hands-on tutorial demonstrates how to combine a classical machine learning pipeline with an agentic AI system to build a hybrid customer retention workflow. It walks through generating a synthetic churn dataset, training a random forest classifier with scikit-learn achieving 91% accuracy, and building an agent powered by a Llama 3.3 model served by Groq that interprets predictions and autonomously triggers retention actions via mock tools. The full example is runnable for free in Google Colab or a local Jupyter notebook.

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