Explainable AI Ops: Enhancing Model Interpretability in Production Virtual Internship
In this virtual internship, students will learn how to incorporate explainable AI techniques like SHAP, LIME, and Anchor into an MLOps pipeline to improve model transparency and interpretability in production environments. They will gain hands-on experience with tools like MLflow, Kubeflow, Airflow, and model monitoring to build a robust and transparent machine learning system.
Track Overview
Tasks & Milestones
Implement SHAP for Model Interpretation
AdvancedIn this task, students will learn how to use the SHAP library to explain the predictions of a machine learning model.
Implement LIME for Local Explanations
AdvancedIn this task, students will learn how to use the LIME library to generate local explanations for individual predictions made by a machine learning model.
Implement Anchor for Global Explanations
AdvancedIn this task, students will learn how to use the Anchor library to generate global explanations for the behavior of a machine learning model.
Implement a Model Monitoring Pipeline
AdvancedIn this task, students will learn how to build a model monitoring pipeline using tools like Airflow and Docker to track the performance of a machine learning model in production.
Evaluate and Improve Model Performance using Explainable AI
AdvancedIn this task, students will use explainable AI techniques to evaluate and improve the performance of a machine learning model in production.
Design and Deploy an Explainable AI-Powered MLOps Pipeline
AdvancedIn this capstone project, students will design and deploy an end-to-end MLOps pipeline that leverages explainable AI techniques to improve model transparency and interpretability.
Prerequisites
- • Proficiency in Python programming
- • Experience with machine learning and model deployment
Certificate
Certificate of Completion
Earn a certificate upon successful completion