Hands-on Workshop: Implementing AI Use Cases with Industrial Data Training Course

Introduction and Team Use Case Selection

• Introduction to AI applications in industrial settings
• Use case categories: quality, maintenance, energy, and logistics
• Forming teams and defining project goals

Understanding and Preparing Industrial Data

• Data types: time-series, tabular, image, and text
• Data collection, cleaning, and preprocessing techniques
• Conducting exploratory data analysis using Pandas and Matplotlib

Model Selection and Prototyping

• Selecting appropriate methods: regression, classification, clustering, or anomaly detection
• Training and assessing models with Scikit-learn
• Applying TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Results

• Designing clear dashboards or reports
• Analyzing performance metrics such as accuracy, precision, and recall
• Recording assumptions and identifying limitations

Deployment Simulation and Feedback

• Simulating edge and cloud deployment scenarios
• Gathering feedback to refine models
• Strategies for integrating solutions into daily operations

Capstone Project Development

• Finalizing and validating team prototypes
• Conducting peer reviews and collaborative debugging
• Preparing project presentations and technical summaries

Team Presentations and Wrap-Up

• Showcasing AI solution concepts and results
• Group reflection on key takeaways
• Planning the roadmap for scaling use cases across the organization

Summary and Next Steps