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Duration 21 hours
Course Outline
Introduction and Selection of Team Use Cases
- Overview of AI applications in industrial settings
- Key use case areas: quality, maintenance, energy, logistics
- Team assembly and definition of project scope
Analyzing and Preparing Industrial Data
- Categories of industrial data: time-series, tabular, image, text
- Data collection, cleansing, and preprocessing techniques
- Exploratory analysis using Pandas and Matplotlib
Selecting Models and Building Prototypes
- Evaluating suitability for regression, classification, clustering, or anomaly detection
- Model training and assessment via Scikit-learn
- Advanced modeling using TensorFlow or PyTorch
Visualizing and Interpreting Outcomes
- Developing user-friendly dashboards or reports
- Understanding performance indicators (accuracy, precision, recall)
- Recording underlying assumptions and constraints
Deployment Simulation and Iterative Feedback
- Modeling edge and cloud deployment scenarios
- Gathering feedback and refining model performance
- Approaches for integrating solutions into operations
Developing Capstone Projects
- Finalizing and validating team prototypes
- Peer assessment and collaborative troubleshooting
- Preparing final presentations and technical documentation
Team Presentations and Conclusion
- Sharing AI solution concepts and results
- Group discussion on insights and lessons learned
- Planning the roadmap for scaling use cases within the organization
Summary and Forward Path
Requirements
- Foundational knowledge of manufacturing or industrial workflows
- Familiarity with Python and core machine learning concepts
- Competence in handling both structured and unstructured data
Target Audience
- Multi-disciplinary teams
- Engineers
- Data scientists
- IT specialists
Testimonials (1)
How to use and explore AI for daily works
