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

Introduction and Selecting Team Use Cases

• Overview of AI applications in industrial settings
• Categorization of use cases: quality, maintenance, energy, and logistics
• Team assembly and defining project goals

Grasping and Preparing Industrial Data

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

Model Selection and Prototyping

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

Visualization and Interpretation of Results

• Designing intuitive dashboards or reports
• Analyzing performance indicators (accuracy, precision, recall)
• Recording assumptions and constraints

Deployment Simulation and Feedback Loop

• Simulating edge and cloud deployment scenarios
• Gathering feedback and optimizing models
• Approaches for integrating with operational workflows

Capstone Project Creation

• Finalizing and testing team prototypes
• Peer reviews and collaborative debugging sessions
• Drafting the project presentation and technical summary

Team Presentations and Concluding Remarks

• Presenting AI solution concepts and achieved outcomes
• Collective reflection and key takeaways
• Planning the roadmap for scaling use cases across the organization

Recap and Future Directions