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Course Outline
Introduction and Selection of Team Use Cases
- Overview of AI applications in industrial settings
- Use case categories: quality, maintenance, energy, and logistics
- Team formation and definition of project scope
Understanding and Preparing Industrial Data
- Types of industrial data: time-series, tabular, image, and text
- Data acquisition, cleansing, and preprocessing techniques
- Exploratory data analysis using Pandas and Matplotlib
Model Selection and Prototyping
- Selecting appropriate methods: regression, classification, clustering, or anomaly detection
- Training and evaluating models using Scikit-learn
- Advanced modeling with TensorFlow or PyTorch
Visualizing and Interpreting Results
- Building intuitive dashboards or reports
- Interpreting performance metrics such as accuracy, precision, and recall
- Documenting key assumptions and limitations
Deployment Simulation and Feedback
- Simulating edge/cloud deployment scenarios
- Gathering feedback and refining models
- Strategies for integrating solutions into daily operations
Capstone Project Development
- Finalizing and testing team prototypes
- Conducting peer reviews and collaborative debugging
- Preparing project presentations and technical summaries
Team Presentations and Conclusion
- Presenting AI solution concepts and outcomes
- Group reflection on key lessons learned
- Roadmap for scaling use cases within the organization
Summary and Next Steps
Requirements
- Familiarity with manufacturing or industrial processes
- Proficiency in Python and fundamental machine learning concepts
- Competence in handling both structured and unstructured data
Audience
- Cross-functional teams
- Engineers
- Data scientists
- IT professionals
21 Hours
