Rolls-Royce's implementation of conditional Generative Adversarial Networks (cGANs) for engineering design optimization represents a significant case study in deploying generative AI in a highly regulated industrial setting. This case study demonstrates how traditional manufacturing companies can leverage modern AI technologies while maintaining strict governance and compliance requirements.

The project focused on a specific challenge in engineering design: how to efficiently explore design spaces and generate new design concepts without going through time-consuming traditional geometry modeling and simulation processes. The solution involved using cGANs to learn from existing simulation data and generate new designs that meet specified conditions.

Technical Implementation:
The system architecture was built on the Databricks Data Intelligence Platform, with several key components and considerations:

• Data Modeling & Preparation
  The system began with careful data modeling to optimize tables for the specific use case. This included:

  • Generation of identity columns
  • Specific table property configurations
  • Management of unique tuples
  • Integration of both successful and unsuccessful design solutions to help the neural network learn what to avoid

• Model Architecture
  The implementation used a 2D representation approach for 3D results, likely to manage computational complexity while still capturing essential design features. The cGAN architecture was specifically chosen for its ability to handle conditional generation, which is crucial for engineering design where specific constraints must be met.

• Production Deployment Considerations
  Several key aspects were considered for production deployment:

  • Model export capabilities to secure environments for sensitive data
  • Transfer learning support for restricted data sets
  • Integration with existing design processes
  • Handling of multi-objective constraints
  • Mechanisms for balancing conflicting design requirements

The implementation leveraged several key MLOps features provided by the Databricks platform:

• MLflow Integration

  • Experiment tracking
  • Model versioning
  • Results sharing
  • Collaborative model tuning
  • Model lifecycle management

• AutoML Capabilities
  The platform's AutoML features were used to:

  • Reduce model training complexity
  • Accelerate development cycles
  • Automate hyperparameter optimization
  • Simplify model deployment processes

• Governance and Security
  The implementation paid special attention to governance through:

  • Unity Catalog implementation for unified data asset management
  • Access control systems for sensitive data
  • Compliance management for aerospace industry requirements
  • Audit trail maintenance

Operational Considerations:
The case study reveals several important operational aspects of running generative AI in production:

• Cost Management
  The platform enabled efficient resource utilization through:

  • Scalable computing resources
  • Optimization of training processes
  • Reduced development cycles
  • Efficient data processing pipelines

• Team Collaboration
  The system supported:

  • Concurrent development by multiple team members
  • Shared access to models and results
  • Collaborative model tuning
  • Knowledge sharing across teams

• Performance Optimization
  Several approaches were used to optimize performance:

  • Rapid assessment of different model architectures
  • Use of specialized packages like Ray for hyperparameter studies
  • Scalability for complex use cases
  • Parallel development capabilities

Challenges and Solutions:
The implementation faced several challenges:

• Multi-objective Optimization

  • Handling potentially conflicting design requirements
  • Balancing different performance metrics
  • Creating solutions that are broadly optimized rather than perfect for single metrics

• Data Security

  • Managing sensitive engineering data
  • Ensuring compliance with export control regulations
  • Protecting intellectual property

• Model Transition

  • Planning for transition from 2D to 3D models
  • Maintaining performance with increased complexity
  • Ensuring scalability of the solution

Results and Impact:
The implementation showed several positive outcomes:

• Faster design iteration cycles
• Reduced costs compared to traditional simulation approaches
• Improved model accuracy through better training data utilization
• Enhanced collaboration capabilities
• Maintained compliance with industry regulations

Future Directions:
The case study indicates several areas for future development:

• Expansion to full 3D model support
• Enhanced multi-objective optimization capabilities
• Further integration with existing design processes
• Improved handling of complex constraints

This case study demonstrates how modern MLOps practices can be successfully applied in traditional engineering environments, balancing innovation with practical constraints and regulatory requirements. It shows the importance of having a robust MLOps platform that can handle not just the technical aspects of AI deployment, but also the governance and compliance requirements that are crucial in industrial applications.