Model Management (training, serving, validation) and Experiment Management for Transportation & Logistics Industries 

This course is designed to provide a comprehensive understanding of model management and experiment management practices in the context of the Transportation & Logistics industries. Participants will learn how to effectively train, serve, validate, and manage machine learning models, as well as design and conduct experiments to improve model performance and robustness. The course covers best practices for model lifecycle management, reproducibility, and scalability, enabling participants to build and maintain reliable AI solutions for various applications in transportation planning, logistics optimization, and supply chain management. 

• Understand the importance and challenges of model management and experiment management in the Transportation & Logistics industries

• Implement version control and organization strategies for machine learning models and datasets

• Design and conduct experiments to optimize model hyperparameters and evaluate model performance

• Apply techniques for model reproducibility, scalability, and deployment in production environments

• Develop model monitoring and maintenance strategies to ensure long-term reliability and performance

1. Introduction to Model Management

• Overview of model management and its importance in the Transportation & Logistics industries

• Model lifecycle and the role of model management

• Version control strategies for machine learning models and datasets

• Model organization and metadata management

• Hands-on exercises: Setting up a version-controlled model repository and organizing model artifacts

2. Model Experimentation and Optimization

• Principles of experimental design and hypothesis testing

• Hyperparameter optimization techniques (e.g., grid search, random search, Bayesian optimization)

• Cross-validation and model evaluation metrics for transportation and logistics applications

• Automated machine learning (AutoML) and its applications in model experimentation

• Hands-on exercises: Conducting model experiments and hyperparameter optimization on transportation and logistics datasets

3. Model Reproducibility and Scalability

• Importance of reproducibility in machine learning and its challenges

• Techniques for ensuring model reproducibility (e.g., containerization, virtual environments)

• Scalable model training and inference using distributed computing frameworks (e.g., Apache Spark, Dask)

• Model serving and deployment strategies for production environments

• Hands-on exercises: Containerizing a machine learning model and deploying it on a scalable infrastructure

4. Model Monitoring and Maintenance

• Importance of model monitoring and maintenance in the Transportation & Logistics industries

• Techniques for detecting model drift and performance degradation

• Strategies for model retraining and updates in production environments

• Model explainability and interpretability techniques for transparent AI solutions

• Hands-on exercises: Implementing a model monitoring and maintenance pipeline for a transportation or logistics use case

• Proficiency in programming with Python and familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

• Understanding of basic machine learning concepts and algorithms

• Knowledge of version control systems (e.g., Git) and containerization technologies (e.g., Docker) is beneficial but not required