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

Introduction and Selection of Team Use Cases

• Overview of AI applications in industrial settings
• Key use case areas: quality, maintenance, energy, and logistics
• Forming teams and defining project goals

Interpreting and Preparing Industrial Data

• Data types: time-series, tabular, image, and text
• Processes for data collection, cleaning, and preprocessing
• Conducting exploratory analysis using Pandas and Matplotlib

Choosing Models and Creating Prototypes

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

Visualizing and Analyzing Outcomes

• Developing clear dashboards or reports
• Understanding performance indicators such as accuracy, precision, and recall
• Recording assumptions and identifying limitations

Simulating Deployment and Gathering Feedback

• Modeling edge and cloud deployment contexts
• Incorporating feedback to refine models
• Planning integration strategies with operational systems

Developing the Capstone Project

• Finalizing and validating team prototypes
• Conducting peer reviews and collaborative troubleshooting
• Preparing project demonstrations and technical summaries

Team Presentations and Concluding Session

• Sharing AI solution concepts and results
• Reflecting on group insights and lessons learned
• Outlining a roadmap for scaling use cases within the organization

Summary and Future Directions