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

Introduction and Selection of Team Use Cases

• Overview of AI integration in industrial settings
• Use case categories: quality control, maintenance, energy, and logistics
• Team formation 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

• Deciding on regression, classification, clustering, or anomaly detection approaches
• Training and assessing models with Scikit-learn
• Utilizing TensorFlow or PyTorch for advanced modeling tasks

Visualizing and Interpreting Results

• Developing intuitive dashboards or reporting tools
• Analyzing performance metrics such as accuracy, precision, and recall
• Recording underlying assumptions and limitations

Deployment Simulation and Feedback

• Simulating edge and cloud deployment scenarios
• Gathering feedback and refining models
• Strategies for integrating solutions into operational workflows

Capstone Project Development

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

Team Presentations and Conclusion

• Presenting AI solution concepts and results
• Group reflection and key takeaways
• Roadmap for scaling applications within the organization

Recap and Future Steps