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

Grasping and Preparing Industrial Data

• Categories of industrial data: time-series, tabular, image, and text
• Data collection, cleansing, and preprocessing techniques
• Conducting exploratory data analysis using Pandas and Matplotlib

Selecting Models and Building Prototypes

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

Visualizing and Making Sense of Results

• Designing clear dashboards or reporting tools
• Understanding performance indicators (accuracy, precision, recall)
• Recording underlying assumptions and potential limitations

Deployment Simulation and Feedback Loops

• Modeling edge and cloud deployment scenarios
• Gathering insights to enhance model performance
• Approaches for integrating solutions into daily operations

Developing the Capstone Project

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

Team Presentations and Closing Remarks

• Sharing AI solution concepts and key outcomes
• Group reflection on insights gained
• Developing a roadmap for expanding use cases within the organization

Recap and Future Directions