Designing and Implementing Enterprise-Grade ML Applications

Outline

Advanced Fine-Tuning and Quantization Techniques for LLMs

• Exploring advanced fine-tuning techniques and architectures for domain-specific LLM adaptation
• Implementing multi-task, meta-learning, and transfer learning techniques for LLM fine-tuning
• Leveraging domain-specific pre-training and intermediate fine-tuning for improved LLM performance
• Quantization and compression techniques for efficient LLM fine-tuning and deployment
• Implementing post-training quantization and pruning techniques for LLM model compression
• Exploring quantization-aware training and other techniques for efficient LLM fine-tuning
• Implementing advanced fine-tuning and quantization techniques for a domain-specific LLM
• Designing and implementing a multi-task fine-tuning architecture with domain-specific pre-training
• Applying quantization and pruning techniques for fine-tuning

Designing and Implementing LLM-Powered Recommender Systems

• Exploring advanced architectures and techniques for LLM-powered recommender systems
• Leveraging LLMs for multi-modal and context-aware recommendation generation
• Implementing hybrid recommender architectures combining LLMs with collaborative and content-based filtering
• Evaluating and optimizing LLM-powered recommender system performance
• Designing and conducting offline and online evaluation studies for LLM-powered recommender systems
• Implementing advanced evaluation metrics and techniques for assessing recommendation quality and diversity
• Hands-on: Building an LLM-powered recommender system for a specific domain

Advanced Model Evaluation, Interpretation, and Debugging Techniques

• Implementing advanced evaluation and benchmarking techniques for LLM-based applications
• Designing and conducting comprehensive evaluation studies with domain-specific metrics and datasets
• Leveraging advanced evaluation frameworks and platforms for automated and reproducible evaluation
• Model interpretation and debugging techniques for understanding LLM behavior and failures
• Implementing advanced model interpretation techniques, such as attention visualization and probing
• Leveraging debugging techniques, such as counterfactual analysis and influence functions, for identifying and mitigating LLM failures
• Conducting an advanced evaluation and debugging study for an LLM-based application
• Designing and implementing a comprehensive evaluation study with domain-specific metrics and datasets
• Applying model interpretation and debugging techniques for LLMs

Ethical Considerations and Responsible AI Practices for Enterprise-Grade LLMs

• Implementing advanced techniques for mitigating biases and ensuring fairness in LLM-based applications
• Leveraging advanced bias detection and mitigation techniques, such as adversarial debiasing and fairness constraints
• Designing and conducting fairness audits and assessments for LLM-based applications
• Ensuring transparency, accountability, and explainability in LLM-based decision-making
• Implementing advanced explainability techniques, such as counterfactual explanations and feature importance
• Designing and implementing governance frameworks and processes for responsible LLM deployment and monitoring
• Conducting an ethical assessment and implementing responsible AI practices for an LLM-based application