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.
• Key use case areas: quality, maintenance, energy, and logistics.
• Team formation and defining project scope.

Analyzing and Preparing Industrial Data

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

Selecting Models and Building Prototypes

• Evaluating approaches: regression, classification, clustering, or anomaly detection.
• Training and assessing models with Scikit-learn.
• Utilizing TensorFlow or PyTorch for complex modeling tasks.

Visualizing and Analyzing Results

• Developing user-friendly dashboards or reports.
• Analyzing key performance indicators (accuracy, precision, recall).
• Documenting underlying assumptions and potential limitations.

Deployment Simulation and Feedback Loop

• Modeling edge and cloud deployment scenarios.
• Gathering feedback to enhance model performance.
• Strategies for seamless integration into operational workflows.

Capstone Project Execution

• Finalizing and testing team prototypes.
• Conducting peer reviews and collaborative debugging.
• Preparing the final project presentation and technical summary.

Team Presentations and Conclusion

• Showcasing AI solution concepts and achieved outcomes.
• Group reflection on key lessons learned.
• Strategic roadmap for scaling use cases across the organization.

Summary and Future Directions