Operationalizing Predictive Analytics in Education
summary
Claims a working MLOps pipeline that moves predictive student risk models from notebooks to production with reproducibility, monitoring and governance and measurable gains in retention engagement and success but offers no concrete effect sizes or sample counts. Describes tooling and architecture: DVC MLflow CI/CD containers and operational pieces like autoscaling drift detection and audit trails. Recommends explainability and compliance using SHAP and LIME model cards drift alerts and shows a continuous retrain loop.
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chapters (14)
- 0:00Introduction and Overview
- 0:18Understanding Predictive Analytics
- 0:37Educational Data Challenges and Opportunities
- 1:21Advantages of MLOps in Education
- 2:14Machine Learning Toolkit for Education
- 3:53Advanced Analytical Approaches
- 6:22Evaluating Predictive Models
- 7:22DevOps Foundations for Machine Learning
- 8:48ML Ops Pipeline Architecture
- 10:23Scalable and Reliable ML Systems
- 11:36Governance and Compliance
- 13:17Integration with Student Support Services
- 14:17Practical Guidance for Implementation
- 15:13Conclusion and Collaboration
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