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

• An understanding of manufacturing or industrial processes
• Experience with Python and basic machine learning
• Ability to work with structured and unstructured data

Audience

• Cross-functional teams
• Engineers
• Data scientists
• IT professionals

AI Use Case Implementation is a hands-on, project-driven approach to applying machine learning, computer vision, and data analytics to solve real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (online or onsite) is aimed at intermediate-level cross-functional teams who wish to collaboratively implement AI use cases aligned with their operational goals and gain experience working with industrial data pipelines.

By the end of this training, participants will be able to:

• Select and scope practical AI use cases from operations, quality, or maintenance.
• Work collaboratively across roles to develop machine learning solutions.
• Handle, clean, and analyze diverse industrial datasets.
• Present a working prototype of an AI-enabled solution based on a selected use case.

Format of the Course

• Interactive lecture and discussion.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction and Team Use Case Selection

• Overview of AI in industrial environments
• Use case categories: quality, maintenance, energy, logistics
• Team formation and scoping of project objectives

Understanding and Preparing Industrial Data

• Types of industrial data: time-series, tabular, image, text
• Data acquisition, cleaning, and preprocessing
• Exploratory data analysis with Pandas and Matplotlib

Model Selection and Prototyping

• Choosing between regression, classification, clustering, or anomaly detection
• Training and evaluating models with Scikit-learn
• Using TensorFlow or PyTorch for advanced modeling

Visualizing and Interpreting Results

• Creating intuitive dashboards or reports
• Interpreting performance metrics (accuracy, precision, recall)
• Documenting assumptions and limitations

Deployment Simulation and Feedback

• Simulating edge/cloud deployment scenarios
• Collecting feedback and improving models
• Strategies for integration with operations

Capstone Project Development

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

Team Presentations and Wrap-Up

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

Summary and Next Steps