Model Development

Choose, train, customize, and evaluate — classic ML and foundation models

• Choosing a Modeling Approach — The decision the exam tests over and over: frame the problem, then pick between built-in algorithms, custom training, pre-trained AI services, and foundation models on Bedrock. (5 lessons, 60 min)
• Training and Tuning on SageMaker AI — What actually happens inside a training job — containers, channels, checkpoints — plus hyperparameter tuning with AMT, distributed training, Spot economics, and fixing fit problems. (4 lessons, 55 min)
• Customizing Foundation Models — The GenAI heart of MLA-C02: prompt engineering as a customization layer, fine-tuning without catastrophic forgetting, continued pre-training, distillation, and retrieval optimization. (4 lessons, 55 min)
• Evaluating Models — Classic and GenAI — Confusion matrices and the classic metric family, baselines and reproducible experiments with MLflow, GenAI evaluation from BLEU to LLM-as-a-judge, and shadow testing before launch. (4 lessons, 60 min)