Large Scale ML & Real-Time Application for Customer Experience (CX) 

This course equips Customer Experience (CX) and Customer Service Management (CSM) professionals with an understanding of Large Scale Machine Learning (ML) and its application in real-time CX initiatives. You'll explore how to handle massive datasets and develop efficient models to deliver immediate value and enhance customer experiences within your organization. 

• Explain the challenges and considerations for applying Machine Learning to large datasets in the context of CX.

• Explore techniques for handling large datasets, including data sampling and distributed computing frameworks.

• Understand different model architectures suitable for real-time applications in CX, such as streaming algorithms and online learning.

• Identify use cases for real-time machine learning in CX, including fraud detection, personalized recommendations, and chatbot interactions.

• Evaluate the trade-offs between model complexity, accuracy, and latency for real-time CX applications.

1. Taming the Data Deluge: Large Scale ML for CX:

• Introduction to Large Scale Machine Learning: Understanding the challenges of processing and analyzing massive customer datasets for building effective AI models in CX initiatives.

• Data Sampling Techniques for Scalability: Exploring techniques like random sampling and stratified sampling to extract manageable subsets from large datasets for model training.

• Case Study 1: Utilizing data sampling to train a customer segmentation model on a massive customer dataset, enabling targeted marketing campaigns.

• Hands-on Session: Working with a simulated large dataset to practice data sampling techniques and explore their impact on model training using a cloud platform (e.g., Google Cloud Platform).

2. Distributed Processing Power for Large Scale ML:

• Distributed Computing Frameworks for Scalability: Introducing distributed computing frameworks like Apache Spark that enable parallel processing of large datasets across multiple machines.

• Demystifying Cloud Platforms for Large Scale ML: Exploring cloud platforms (e.g., AWS, Azure) that provide pre-built tools and infrastructure for efficient large scale machine learning.

• Case Study 2: Leveraging a cloud platform to train a real-time recommendation model on a massive product data set, enabling personalized product suggestions for customers.

• Guest Speaker Session: Inviting a data scientist who has experience with large scale ML for CX applications to share their experience with distributed computing frameworks and cloud platforms.

• Group Discussion: Brainstorming potential CX applications within your department that could benefit from large scale machine learning and identifying potential data sources.

3. Real-Time Applications: When Speed Matters in CX:

• Model Architectures for Real-Time Decisions: Exploring model architectures suitable for real-time applications, such as streaming algorithms and online learning techniques.

• Understanding Latency and its Impact on CX: Discussing the concept of latency (response time) and its critical role in ensuring a seamless customer experience in real-time applications.

• Case Study 3: Utilizing a streaming algorithm to detect fraudulent transactions in real-time, protecting customers and preventing financial losses.

• Interactive Workshop: Working with a pre-built real-time model (e.g., anomaly detection) and exploring its functionality and considerations for deployment in a CX application.

• Introduction to Model Optimization Techniques: Understanding techniques for optimizing models to reduce latency and improve performance for real-time CX applications.

4. The Future of Large Scale ML and Responsible AI in CX:

• Emerging Trends in Large Scale ML: Exploring advancements in large scale machine learning techniques and their potential future applications in areas like real-time sentiment analysis and personalized chatbot interactions.

• Balancing Accuracy & Latency in CX: Evaluating the trade-offs between model complexity, accuracy, and latency for real-time CX applications, finding the optimal balance for specific use cases.

• Responsible AI for Real-Time CX: Discussing the importance of responsible AI practices in real-time applications, considering fairness, explainability, and data privacy in customer interactions.

• Course Wrap-up & Project Presentations: Teams propose a real-time CX application leveraging large scale machine learning. Their presentations should outline the chosen use case, data considerations, model architecture, potential challenges, and responsible AI considerations.

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

• Understanding of distributed computing concepts and big data technologies (e.g., Apache Hadoop, Apache Spark)

• Knowledge of real-time data processing and streaming frameworks (e.g., Apache Kafka, Apache Flink) is beneficial but not required