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

Introduction and Selection of Team Use Cases

• Context of AI within industrial settings
• Categories of use cases: quality, maintenance, energy, and logistics
• Forming teams and defining project goals

Grasping and Preparing Industrial Data

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

Model Selection and Prototyping

• Deciding among regression, classification, clustering, or anomaly detection
• Training and assessing models with Scikit-learn
• Employing TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Outcomes

• Building clear dashboards or reports
• Understanding performance indicators (accuracy, precision, recall)
• Recording assumptions and recognizing limitations

Deployment Simulation and Review

• Simulating edge and cloud deployment contexts
• Gathering insights and refining models
• Approaches for integrating solutions into operational workflows

Capstone Project Creation

• Finalizing and validating team prototypes
• Peer evaluation and joint troubleshooting
• Drafting the project presentation and technical summary

Team Presentations and Conclusion

• Sharing AI solution concepts and results
• Collective reflection and key takeaways
• Planning the path to scaling use cases across the organization

Recap and Future Directions