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 assembly and definition of project scope

Grasping and Preparing Industrial Data

• Data formats in industry: time-series, tabular, image, and text
• Acquiring, cleansing, and preprocessing data
• Conducting exploratory data analysis using Pandas and Matplotlib

Selecting Models and Building Prototypes

• Choosing the appropriate approach: regression, classification, clustering, or anomaly detection
• Training and assessing models with Scikit-learn
• Leveraging TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Outcomes

• Designing intuitive dashboards or reports
• Analyzing performance indicators such as accuracy, precision, and recall
• Recording assumptions and identifying limitations

Simulating Deployment and Gathering Feedback

• Modeling edge and cloud deployment environments
• Obtaining feedback to refine and enhance models
• Approaches for integrating solutions into operational workflows

Developing the Capstone Project

• Completing and validating team prototypes
• Conducting peer reviews and collaborative debugging
• Preparing project presentations and technical summaries

Team Presentations and Conclusion

• Showcasing AI solution concepts and results
• Group debriefing and key takeaways
• Planning the roadmap for scaling use cases across the organization

Recap and Future Directions