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

Introduction and Selection of Team Use Cases

• Overview of AI applications in industrial settings
• Categories of use cases: quality, maintenance, energy, and logistics
• Team formation and definition of project goals

Understanding and Preparation of 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 between regression, classification, clustering, or anomaly detection methods
• Training and evaluating models using Scikit-learn
• Leveraging TensorFlow or PyTorch for advanced modeling tasks

Visualization and Interpretation of Results

• Designing intuitive dashboards or reports
• Interpreting key performance metrics such as accuracy, precision, and recall
• Documenting underlying assumptions and limitations

Deployment Simulation and Feedback Integration

• Simulating edge and cloud deployment scenarios
• Gathering feedback and refining models accordingly
• Strategies for integrating solutions into operational workflows

Capstone Project Development

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

Team Presentations and Conclusion

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

Summary and Future Steps