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 objectives

Comprehending and Preparing Industrial Data

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

Selecting Models and Building Prototypes

• Selecting appropriate methods: regression, classification, clustering, or anomaly detection
• Training and assessing models using Scikit-learn
• Leveraging TensorFlow or PyTorch for complex modeling

Visualizing and Interpreting Outcomes

• Developing intuitive dashboards or reports
• Analyzing performance metrics (accuracy, precision, recall)
• Recording assumptions and recognizing limitations

Deployment Simulation and Review

• Modeling edge and cloud deployment scenarios
• Gathering feedback and refining models
• Strategies for integrating solutions into operations

Developing the Capstone Project

• Finalizing and testing team prototypes
• Peer evaluation and collaborative debugging
• Preparing the project presentation and technical summary

Team Presentations and Conclusion

• Presenting AI solution concepts and results
• Group reflection and key takeaways
• Planning a roadmap for scaling use cases within the organization

Recap and Future Directions