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

Introduction and Team-Based Use Case Selection

• Overview of AI applications in industrial settings
• Use case categories: quality, maintenance, energy, and logistics
• Team formation and defining project scope and objectives

Understanding and Preparing Industrial Data

• Types of industrial data: time-series, tabular, image, and text
• Data acquisition, cleaning, and preprocessing techniques
• Exploratory data analysis using Pandas and Matplotlib

Model Selection and Prototyping

• Selecting appropriate models: regression, classification, clustering, or anomaly detection
• Training and evaluating models using Scikit-learn
• Leveraging TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Results

• Building intuitive dashboards or generating reports
• Interpreting key performance metrics (accuracy, precision, recall)
• Documenting assumptions and identifying limitations

Deployment Simulation and Feedback

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

Capstone Project Development

• Finalizing and testing team prototypes
• Peer review and collaborative debugging sessions
• Preparing project presentations and technical summaries

Team Presentations and Wrap-Up

• Presenting AI solution concepts and final outcomes
• Group reflection and key lessons learned
• Developing a roadmap for scaling use cases within the organization

Summary and Next Steps