EE402 — Explainable AI and MLOps for Energy

The Two Senior-Level Capabilities Every Energy AI Practitioner Needs

EE402 is the bridge between AI development skills and AI deployment skills. It addresses the two questions that determine whether AI actually delivers value in the energy sector: Can you explain why the model made that decision? and Can you keep the model working reliably in production over months and years?

The first question (explainability) is non-negotiable in regulated industries. When a utility's AI flags a transformer for replacement, when a forecast triggers a capacity decision, when a market model recommends a bid strategy, "the AI said so" is unacceptable. Engineers, regulators, and executives need to understand why. This course teaches you to make AI decisions transparent and auditable using SHAP, LIME, and other modern explanation methods.

The second question (MLOps) is what separates impressive demos from production AI systems. Most AI projects in the energy sector fail not because the model didn't work in development but because nobody managed the model lifecycle once it was deployed. This course teaches you to operate AI systems through their full lifecycle: versioning, monitoring, drift detection, and retraining.

EE402 is required for both CAGP (Certified AI for Grid Professional) and CAEP (Certified AI for Energy Professional) certifications. Engineers who complete this course can defend their AI decisions to regulators, lead the AI deployment effort within their organization, and operate AI systems through their full lifecycle with confidence.

What You Will Learn

• Apply SHAP (SHapley Additive exPlanations) for per-prediction and global model explanation
• Apply LIME (Local Interpretable Model-agnostic Explanations) for local interpretability
• Diagnose why a model made a specific prediction in regulatory and audit contexts
• Implement complete MLOps pipelines: data versioning, experiment tracking, model registry, CI/CD
• Detect and respond to data drift and concept drift in production AI models
• Design retraining pipelines appropriate to different model types and operational tempos
• Build monitoring dashboards that surface model health to engineering and operations teams
• Establish governance practices that satisfy regulators and internal audit requirements

Course Structure

EE402 is organized into four modules that progress from explainability fundamentals through full production AI operations:

• Module 1: Why Explainability Matters in Energy — The regulatory, operational, and ethical case for explainable AI. Why "the AI said so" is unacceptable in safety-critical and financially-significant applications.
• Module 2: SHAP, LIME, and Explanation Methods — Hands-on application of the most important model explanation techniques. Local versus global explanation. Practical limitations and when each method fits.
• Module 3: MLOps Foundations — Data versioning, experiment tracking, model registry, deployment pipelines. The infrastructure that makes production AI possible.
• Module 4: Production AI Lifecycle — Drift detection, monitoring, alerting, retraining triggers, and governance. How to keep AI working reliably at month six and year three, not just at launch.

Real-World Examples

Every concept is grounded in energy applications. See how SHAP values explain why a transformer health model flagged specific units for replacement, satisfying regulator questions. Learn how a major utility detected concept drift in a load forecasting model after weather pattern shifts and triggered automatic retraining. See an MLOps architecture for a 50-substation predictive maintenance deployment with continuous monitoring. Real production scenarios, real organizational challenges, real engineering decisions.

Who This Course Is For

• Senior engineers leading AI projects in regulated energy environments
• Engineers preparing AI systems for regulatory submissions or audit defense
• Engineering leads building or operating production AI systems
• AI champions within utilities responsible for governance and lifecycle management
• Anyone pursuing CAGP or CAEP certification
• Engineers transitioning from AI development to AI operations roles

Prerequisites

• EE400 — AI and Machine Learning Fundamentals for Energy (required; understanding of model evaluation and overfitting is foundational here)
• EE401 — Deep Learning, LLMs and Generative AI for Energy (recommended; helps with the deep learning explainability discussion)
• Engineering or technical background
• Basic familiarity with software development concepts (helpful for the MLOps modules but not required at a coding level)

Format and Access

• Duration: Approximately 8 hours of content
• Format: Self-paced online with video instruction, demonstrations, and quizzes
• Course Access: 6 months of full access from enrollment
• Completion Window: 90 days to complete coursework and the final exam
• Assessment: 4 module quizzes (30% of grade) + comprehensive final exam (70% of grade)
• Passing Score: 70% overall
• Language: English
• AI Tools: Encouraged for learning and exercises; prohibited during quizzes and the final exam

Path to Certification

EE402 is the third course in GIEE's AI in Energy Core curriculum and contributes to two professional certifications:

• CAGP — Certified AI for Grid Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Grid Add-On courses (EE410, EE411, EE412), then pass the CAGP certification exam.
• CAEP — Certified AI for Energy Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Energy Add-On courses (EE420, EE421, EE422), then pass the CAEP certification exam.