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
• Use case categories: quality, maintenance, energy, and logistics
• Forming teams and defining project scope

Comprehending and Preparing Industrial Data

• Types of industrial data: time-series, tabular, images, and text
• Data collection, cleansing, and preprocessing techniques
• Exploratory data analysis using Pandas and Matplotlib

Model Selection and Prototype Development

• Selecting between regression, classification, clustering, or anomaly detection
• Training and assessing models with Scikit-learn
• Utilizing TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Outcomes

• Developing intuitive dashboards or reporting formats
• Analyzing performance metrics (accuracy, precision, recall)
• Recording assumptions and identifying limitations

Deployment Simulation and Review

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

Capstone Project Execution

• Finalizing and validating team prototypes
• Peer evaluation and collaborative troubleshooting
• Preparing the final project presentation and technical summary

Team Presentations and Conclusion

• Presenting AI solution concepts and results
• Group reflection on key learnings
• Creating a roadmap for scaling use cases across the organization

Recap and Future Directions