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
• Categories of use cases: quality, maintenance, energy, and logistics
• Team assembly and definition of project scope

Understanding and Preparing Industrial Data

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

Model Selection and Prototyping

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

Visualizing and Interpreting Results

• Designing intuitive dashboards or reports
• Analyzing performance indicators such as accuracy, precision, and recall
• Documenting underlying assumptions and system limitations

Deployment Simulation and Feedback Loops

• Simulating edge and cloud deployment scenarios
• Gathering feedback and iteratively improving models
• Strategies for integrating AI into operational workflows

Capstone Project Development

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

Team Presentations and Closing

• Presenting AI solution concepts and achieved outcomes
• Group reflection on key takeaways and lessons learned
• Developing a roadmap for scaling use cases across the organization

Summary and Recommended Next Steps