Advanced Transformers

This course is designed to provide a comprehensive understanding of advanced transformer architectures and their applications in Production Control and Operations (P&OC). Participants will learn about the latest developments in transformer models, such as attention mechanisms, pre-training techniques, and domain-specific adaptations, enabling them to develop and deploy state-of-the-art natural language processing (NLP) and sequence modeling solutions for various tasks relevant to production scheduling, inventory management, and supply chain optimization.

• Understand the principles and techniques behind advanced transformer architectures

• Implement and fine-tune pre-trained transformer models, such as BERT, GPT, and T5, for P&OC tasks

• Apply domain-specific adaptations to transformer models, such as incorporating production data and operational constraints

• Develop transformer-based solutions for production scheduling, inventory management, and supply chain optimization

• Deploy transformer models for real-time applications in Production Control and Operations

1. Attention Mechanisms and Pre-training

• Overview of attention mechanisms and their role in transformers

• Self-attention, multi-head attention, and cross-attention

• Pre-training objectives: masked language modeling, next sentence prediction, and contrastive learning

• Hands-on exercises: Implementing attention mechanisms and pre-training techniques from scratch

2. Advanced Transformer Architectures

• BERT (Bidirectional Encoder Representations from Transformers) and its variants for P&OC text data

• GPT (Generative Pre-trained Transformer) and its applications in production text generation

• T5 (Text-to-Text Transfer Transformer) and its unified framework for P&OC tasks

• Hands-on exercises: Fine-tuning pre-trained transformer models for production scheduling and inventory management

3. Domain-Specific Adaptations

• Incorporating production data (e.g., demand forecasts, resource constraints) into transformer models

• Injecting operational knowledge and business rules into transformer architectures

• Domain-adaptive pre-training and transfer learning for P&OC-specific language models

• Hands-on exercises: Developing domain-specific transformer models for supply chain optimization and demand forecasting

4. Applications in Production Scheduling and Inventory Management

• Transformer-based models for job shop scheduling and resource allocation

• Inventory level prediction and replenishment using transformer models

• Integrating transformers with optimization techniques for production scheduling and inventory management

• Hands-on exercises: Building a transformer-based solution for production scheduling or inventory management

5. Deployment and Practical Considerations

• Deploying transformer models in production environments for real-time inference

• Optimizing transformer models for resource-constrained settings (e.g., edge devices, IoT platforms)

• Monitoring and updating deployed models for continuous improvement

• Ethical considerations and interpretability techniques for transformer models in P&OC

• Hands-on exercises: Deploying a transformer model using a cloud platform (e.g., AWS, GCP) and integrating it with a production system or supply chain data pipeline

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic NLP concepts and techniques (e.g., tokenization, word embeddings)

• Knowledge of the original transformer architecture and its applications

• Knowledge of production control and operations management principles