Model Management & Experience Management for Customer Experience (CX) 

This course equips Customer Experience (CX) and Customer Service Management (CSM) professionals with the fundamentals of Model Management and Experiment Management. You'll explore the critical processes for training, serving, validating, and continuously improving AI models used to enhance customer experiences within your organization. 

• Explain the key stages of Model Management, including training, serving, validation, and monitoring for ensuring effective and reliable AI models in CX applications.

• Understand the importance of Experiment Management for iteratively improving AI models and selecting the best performing models for deployment in CX tasks.

• Explore various tools and techniques for model training (e.g., cloud platforms, version control) and deployment (e.g., APIs, containerization).

• Identify key metrics for evaluating model performance and potential biases in the context of CX applications.

• Develop best practices for monitoring models in production to ensure continuous improvement and mitigate potential risks.

1. The AI Model Lifecycle: From Training to Serving:

• Introduction to Model Management for CX: Understanding the importance of a structured approach for managing AI models throughout their lifecycle, from development to deployment in CX applications.

• Unveiling the Training Process: Exploring the essential steps of model training, including data preparation, hyperparameter tuning, and selecting appropriate training frameworks.

• Case Study 1: Optimizing the training process for a customer churn prediction model, leading to improved model performance and reduced customer loss.

• Hands-on Session: Experimenting with a simple model training exercise on a sample customer dataset using a cloud platform (e.g., Google Colab).

2. From Models to Applications: Deployment & Serving:

• Deployment Strategies for CX Applications: Exploring different deployment options for AI models (e.g., APIs, containerization) to integrate them seamlessly into customer-facing applications.

• Understanding Model Serving Infrastructure: Learning about the infrastructure considerations for serving models in production, ensuring scalability and real-time performance for CX tasks.

• Case Study 2: Deploying a sentiment analysis model as an API to analyze customer feedback in real-time, enabling faster response to customer concerns.

• Guest Speaker Session: Inviting an AI engineer or MLOps specialist to share their experience with deploying and serving AI models for CX applications.

• Group Discussion: Identifying potential CX applications within your department where AI models can be deployed and brainstorming deployment strategies.

3. Ensuring Model Performance & Fairness: Validation & Monitoring:

• Validation Techniques for Robust AI: Exploring various validation techniques (e.g., k-fold cross-validation) to assess model performance and identify potential overfitting or bias issues.

• Understanding Model Fairness in CX: Discussing the importance of fairness in AI models used for CX tasks and exploring techniques for mitigating bias.

• Case Study 3: Utilizing validation techniques to identify and address bias in a customer recommendation model, ensuring fair treatment for all customers.

• Interactive Workshop: Working with a pre-trained model to perform model validation and explore fairness considerations for a chosen CX application.

• Introduction to Model Monitoring: Understanding the importance of monitoring models in production to detect performance degradation and ensure model reliability over time.

4. Experiment Management for Continuous Improvement:

• The Power of Experiment Management: Understanding the role of Experiment Management in iteratively improving AI models and selecting the best performing models for deployment.

• A/B Testing for Data-Driven Decision Making: Learning about A/B testing techniques to compare different model versions and identify the most effective model for CX tasks.

• Case Study 4: Utilizing A/B testing to compare two chatbot models for customer service inquiries, selecting the model that leads to higher customer satisfaction.

• Course Wrap-up & Project Presentations: Teams present their chosen CX application and outline their plan for model management, including training, deployment, validation, monitoring, and potential A/B testing strategies.

• Resource Sharing: Discussing best practices and ongoing learning opportunities for staying up-to-date with Model Management and Experiment Management techniques for CX applications.

• 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