Module 5: Model Development | PMI-CPMAI Certification Prep

Overview

This module covers the core process of building and training AI models. You will learn algorithm selection criteria, model training techniques, hyperparameter optimization strategies, and model architecture design principles. Model development is an iterative experimental process that requires careful tracking and documentation of experiments and results.

Learning Objectives

Select appropriate algorithms based on problem type, data characteristics, and business requirements
Implement model training pipelines with proper data splitting and validation strategies
Apply hyperparameter tuning techniques to optimize model performance
Design model architectures appropriate for the specific AI task and constraints
Utilize transfer learning to leverage pre-trained models for faster development

Key Concepts

Algorithm Selection

Algorithm selection depends on the problem type (classification, regression, clustering, recommendation), data characteristics (size, dimensionality, structure), performance requirements (accuracy, speed, interpretability), and resource constraints.

Algorithm Categories:

• Traditional ML: Random Forest, XGBoost, SVM • Neural Networks: CNN, RNN, Transformers • Ensemble Methods: Boosting, Bagging, Stacking • Specialized: Collaborative Filtering, Reinforcement Learning

Model Training

Model training involves feeding prepared data to the selected algorithm to learn patterns. Key considerations include proper data splitting (train/validation/test), handling class imbalance, preventing data leakage, and monitoring training progress.

Training Set

• 60-80% of data
• Model learning
• Hyperparameter tuning

Validation Set

• 10-20% of data
• Model selection
• Early stopping

Test Set

• 10-20% of data
• Final evaluation
• Performance reporting

Hyperparameter Tuning

Hyperparameters are settings that control the learning process (not learned from data). Tuning strategies include grid search (exhaustive), random search (probabilistic), Bayesian optimization (efficient), and manual search (domain-guided). The project manager should ensure adequate compute resources and tracking for hyperparameter experiments.

Transfer Learning

Transfer learning leverages pre-trained models on related tasks, significantly reducing training time and data requirements. Common approaches include fine-tuning pre-trained foundations (BERT, GPT, ResNet) or using embeddings from established models. Transfer learning is particularly valuable when labeled data is scarce.

Example Scenario

"For the recommendation engine, the team selects a hybrid approach combining collaborative filtering (for users with sufficient history) and content-based filtering (for new users). They implement matrix factorization using ALS with 50 latent factors. Hyperparameter tuning explores regularization values (0.01-1.0), learning rates (0.001-0.1), and embedding sizes (32-128). Training uses early stopping with patience of 5 epochs on validation RMSE. The team also experiments with a neural network variant using transfer learning from a pre-trained item embedding trained on the product catalog."

Summary

Module 5 has covered the essential model development techniques:

• Algorithm selection requires understanding problem types and constraints
• Proper data splitting prevents overfitting and ensures valid evaluation
• Hyperparameter tuning optimizes model performance systematically
• Transfer learning accelerates development with pre-trained models
• Experiment tracking is essential for reproducibility and collaboration