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

Introduction and Selection of Team Use Cases

• The role of AI in industrial settings
• Application areas: quality, maintenance, energy, and logistics
• Team formation and defining project goals

Handling and Preparing Industrial Data

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

Selecting Models and Building Prototypes

• Choosing the right approach: regression, classification, clustering, or anomaly detection
• Training and assessing models with Scikit-learn
• Leveraging TensorFlow or PyTorch for advanced modeling

Visualizing and Analyzing Results

• Designing clear dashboards or reports
• Understanding performance indicators such as accuracy, precision, and recall
• Recording assumptions and constraints

Deployment Simulation and Review

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

Developing the Capstone Project

• Finalizing and validating team prototypes
• Peer evaluation and collaborative troubleshooting
• Preparing the final presentation and technical documentation

Team Presentations and Conclusion

• Showcase AI solution concepts and results
• Collective reflection and key takeaways
• Planning the roadmap for expanding use cases within the organization

Recap and Future Steps