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

Introduction and Selecting Team Use Cases

• Overview of AI applications in industrial settings
• Use case domains: quality, maintenance, energy, and logistics
• Team assembly and defining project goals

Understanding and Preparing Industrial Data

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

Model Selection and Prototyping

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

Visualizing and Interpreting Outcomes

• Developing intuitive dashboards or analytical reports
• Analyzing performance indicators (accuracy, precision, recall)
• Recording assumptions and identifying limitations

Deployment Simulation and Review

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

Capstone Project Development

• Finalizing and validating team prototypes
• Conducting peer reviews and collaborative troubleshooting
• Preparing the final project presentation and technical overview

Team Presentations and Conclusion

• Showcasing AI solution concepts and results
• Group discussion on key takeaways and lessons learned
• Planning the roadmap for scaling use cases across the organization

Recap and Future Directions