Standardize all module introductions and fix agent structure

Module Standardization:
- Applied consistent introduction format to all 17 modules
- Every module now has: Welcome, Learning Goals, Build→Use→Reflect, What You'll Achieve, Systems Reality Check
- Focused on systems thinking, performance, and production relevance
- Consistent 5 learning goals with systems/performance/scaling emphasis

Agent Structure Fixes:
- Recreated missing documentation-publisher.md agent
- Clear separation: Documentation Publisher (content) vs Educational ML Docs Architect (structure)
- All 10 agents now present and properly defined
- No overlapping responsibilities between agents

Improvements:
- Consistent Build→Use→Reflect pattern (not Understand or Analyze)
- What You'll Achieve section (not What You'll Learn)
- Systems Reality Check in every module
- Production context and performance insights emphasized

modules/source/15_mlops/mlops_dev.py

@@ -10,21 +10,33 @@
 
 # %% [markdown]
 """
-# MLOps - Production ML Systems
+# MLOps - Production Deployment and Lifecycle Management
 
-Welcome to the MLOps module! This is where we close the loop on the complete ML system lifecycle.
+Welcome to the MLOps module! You'll build the production infrastructure that deploys, monitors, and maintains ML systems over time, completing the full ML systems engineering lifecycle.
 
 ## Learning Goals
-- Understand why ML models degrade over time without maintenance
-- Implement performance monitoring and drift detection systems
-- Build automated retraining triggers that use your training pipeline
-- Create model comparison and deployment workflows
-- See how all TinyTorch components work together in production
+- Systems understanding: How ML models degrade in production and why continuous monitoring and maintenance are critical for system reliability
+- Core implementation skill: Build deployment, monitoring, and automated retraining systems that maintain model performance over time
+- Pattern recognition: Understand how data drift, model decay, and system failures affect production ML systems
+- Framework connection: See how your MLOps implementation connects to modern platforms like MLflow, Kubeflow, and cloud ML services
+- Performance insight: Learn why operational concerns often dominate technical concerns in production ML systems
 
-## Build → Use → Deploy
-1. **Build**: Complete MLOps infrastructure for model lifecycle management
-2. **Use**: Deploy and monitor ML systems that automatically respond to issues
-3. **Deploy**: Create production-ready systems that maintain themselves over time
+## Build → Use → Reflect
+1. **Build**: Complete MLOps infrastructure with deployment, monitoring, drift detection, and automated retraining capabilities
+2. **Use**: Deploy TinyTorch models to production-like environments and observe how they behave over time
+3. **Reflect**: Why do most ML projects fail in production, and how does proper MLOps infrastructure prevent system failures?
+
+## What You'll Achieve
+By the end of this module, you'll understand:
+- Deep technical understanding of how production ML systems fail and what infrastructure prevents these failures
+- Practical capability to build MLOps systems that automatically detect and respond to model degradation
+- Systems insight into why operational complexity often exceeds algorithmic complexity in production ML systems
+- Performance consideration of how monitoring overhead and deployment latency affect user experience
+- Connection to production ML systems and how companies manage thousands of models across different environments
+
+## Systems Reality Check
+💡 **Production Context**: Companies like Netflix and Uber run thousands of ML models in production, requiring sophisticated MLOps platforms to manage deployment, monitoring, and retraining at scale
+⚡ **Performance Note**: Production ML systems spend more computational resources on monitoring, logging, and infrastructure than on actual model inference - operational overhead dominates
 """
 
 # %% nbgrader={"grade": false, "grade_id": "mlops-imports", "locked": false, "schema_version": 3, "solution": false, "task": false}