Model Management & Experience Management

This course dives into the critical stages beyond building AI models – effectively managing their training, serving, validation, and experimentation within Supply Chain Management (SCM). You'll explore best practices for ensuring robust, reliable, and continually improving AI solutions that optimize your supply chains.

• Define the importance of Model Management for robust and reliable AI applications in SCM.

• Explore the Model Training Lifecycle: Optimizing training processes for efficient and effective model development.

• Understand Model Serving Strategies: Deploying trained models for real-world use cases in your supply chains.

• Gain insights into Model Validation Techniques: Continuously evaluating model performance and addressing potential issues.

• Master Experiment Management: Designing and conducting controlled experiments to compare and improve AI models for SCM tasks.

1. The Lifecycle of an AI Model

• Introduction to Model Management: Ensuring the smooth transition from development to real-world use in SCM.

• Demystifying the Model Training Lifecycle: Optimization techniques like hyperparameter tuning and data versioning.

• Hands-on Exercises (Optional): Utilizing online training platforms or cloud tools to explore basic model training optimization techniques (may require basic coding).

• Model Serving Strategies: Understanding deployment options like cloud platforms or on-premise solutions for SCM applications.

• Case Studies: Exploring real-world examples of deploying AI models for tasks like demand forecasting or inventory optimization in SCM.

2. Ensuring Model Performance and Continuous Improvement

• Deep dive into Model Validation Techniques: Monitoring model performance metrics and identifying potential biases or drift in real-world use.

• Understanding Explainable AI (XAI) Techniques: Making AI models more interpretable for stakeholders in SCM.

• Introduction to Experiment Management: Designing controlled experiments to compare different AI models and identify the best solution.

• Hands-on Exercises (Optional): Utilizing online tools or model management platforms to explore basic experiment design and analysis for SCM tasks (may involve basic scripting).

• Course Wrap-up: Addressing challenges in Model Management and responsible AI practices in SCM implementations.

• 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