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

Introduction and Team Scenario Selection

• Insights into AI applications in industrial settings
• Categories of use cases: quality, maintenance, energy, and logistics
• Formation of teams and definition of project goals

Analyzing and Preparing Industrial Data

• Varieties of industrial data: time-series, tabular, image, and text
• Methods for data acquisition, cleaning, and preprocessing
• Exploratory data analysis using Pandas and Matplotlib

Model Choice and Prototype Development

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

Visualization and Interpretation of Outcomes

• Designing clear dashboards or reports
• Evaluating performance indicators (accuracy, precision, recall)
• Recording assumptions and identifying limitations

Deployment Simulation and Feedback Loop

• Simulating edge and cloud deployment contexts
• Gathering feedback to refine models
• Strategies for seamless operational integration

Capstone Project Progression

• Finalizing and testing team prototypes
• Peer evaluation and collaborative troubleshooting
• Preparing project presentations and technical summaries

Team Presentations and Conclusion

• Presenting AI solution concepts and results
• Group reflection on key learnings
• Roadmap for scaling use cases within the organization

Recap and Subsequent Steps