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.
• Use case categories including quality, maintenance, energy, and logistics.
• Team formation and defining project objectives.

Understanding and Preparing Industrial Data

• Types of industrial data: time-series, tabular, image, and text.
• Data acquisition, cleaning, and preprocessing techniques.
• Performing exploratory data analysis using Pandas and Matplotlib.

Model Selection and Prototyping

• Selecting appropriate models: 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

• Developing intuitive dashboards or reporting mechanisms.
• Analyzing performance metrics such as accuracy, precision, and recall.
• Documenting underlying assumptions and limitations.

Deployment Simulation and Feedback

• Simulating edge and cloud deployment scenarios.
• Gathering feedback to refine and improve models.
• Strategies for integrating solutions into daily operations.

Capstone Project Development

• Finalizing and testing team prototypes.
• Conducting peer reviews and collaborative debugging sessions.
• Preparing project presentations and technical summaries.

Team Presentations and Conclusion

• Presenting AI solution concepts and resulting outcomes.
• Group reflection on key lessons learned.
• Roadmap for scaling AI use cases within the organization.

Summary and Future Steps