Evaluating AI Models in the Real World: ML, LLMs, and GenAI

About this course

Stop guessing. Start evaluating like a production pro.

Most courses teach you how to build models. Almost none teach you how to decide if a model is actually good enough to ship — or how to keep it healthy once it's live. That's the gap this course fills.

Production Model Evaluation & Monitoring: ML, AI, LLMs & GenAI is an advanced, scenario-based practice course built for practitioners who need to make real decisions: Which metric should I use? Which model should I promote? Why did performance drop? Is my LLM hallucinating more? What do I do now?

Through realistic case studies drawn from credit risk, fraud detection, recommendation systems, document extraction, RAG, and agent workflows, you'll work through the exact questions hiring managers ask and production teams face daily. Every scenario is tool-agnostic, so the skills you build apply to any stack, any industry, and any model type — classical ML, deep learning, or GenAI.What you'll master:

• Selecting the right metric for the right scenario — ROC-AUC vs. PR-AUC, F1, calibration, NDCG, WAPE, and cost-sensitive evaluation
• Designing offline, out-of-time, shadow, canary, and A/B tests to compare champion vs. challenger models
• Making promotion decisions using ML metrics, business KPIs, risk guardrails, and fairness checks
• Monitoring production models for data quality, data drift, concept drift, prediction drift, and operational health
• Diagnosing performance degradation systematically — from pipeline bugs and label delay to feature drift and segment-level failures
• Evaluating LLM and GenAI solutions: summarization, RAG, document extraction, and agents
• Measuring faithfulness, groundedness, hallucination, relevance, and safety
• Building field-level JSON extraction evaluation with schema validity, fuzzy matching, critical-field weighting, and human review routing<

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.