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

Introduction and Team Scenario Selection

• Overview of AI applications in industrial settings
• Scenario categories: quality, maintenance, energy, logistics
• Formation of teams and definition of project goals

Comprehending and Preparing Industrial Data

• Data types in industry: time-series, tabular, image, text
• Data collection, cleansing, and preprocessing techniques
• Exploratory data analysis using Pandas and Matplotlib

Model Selection and Prototype Development

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

Visualization and Interpretation of Outcomes

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

Deployment Simulation and Review

• Simulating edge and cloud deployment contexts
• Gathering feedback and refining models
• Approaches for integrating with operational processes

Capstone Project Development

• Finalizing and validating team prototypes
• Peer assessment and collaborative debugging
• Preparing project presentations and technical overviews

Team Presentations and Conclusion

• Sharing AI solution concepts and results
• Group reflection and analysis of learnings
• Strategic roadmap for scaling scenarios within the organization

Summary and Subsequent Actions