WA3518
Designing and Implementing Enterprise-Grade ML Applications Training
This advanced Machine Learning (ML) course is designed for Data Science and ML professionals who want to master designing and implementing enterprise-grade ML applications. Attendees learn how to evaluate advanced LLM architectures and dive into advanced topics, such as fine-tuning and quantization techniques, LLM-powered recommender systems, model evaluation, and debugging, as well as ethical considerations and responsible AI practices for enterprise-grade LLMs.
Course Details
Duration
4 days
Prerequisites
- Practical programming skills in Python and familiarity with LLM concepts and frameworks (3+ Months LLM, 6+ Months Python and Machine Learning)
- LLM Access via API (OpenAI), Open Source Libraries (HuggingFace)
- LLM Application development experience (RAG, Chatbots, etc)
- Strong practical understanding of ML concepts, algorithms, and evaluation
- Supervised Learning, Unsupervised Learning, and respective algorithms
- Statistics, Probability, and Linear Algebra (vectors) foundations
- Experience with at least one deep learning framework (e.g., TensorFlow, PyTorch)
Skills Gained
- Produce high-performing, domain-specific LLMs through advanced fine-tuning techniques
- Deploy efficient LLM models in resource-constrained environments through effective model compression
- Develop LLM-powered recommender systems that deliver personalized, context-aware user experiences
- Quantify LLM-based application performance, identifying areas for improvement and optimization
- Diagnose and enhance LLM models through in-depth interpretation and robust debugging techniques
- Build fair and unbiased LLM-based applications through advanced bias mitigation strategies
- Ensure transparency, accountability, and explainability in LLM-based applications, adhering to responsible AI principles
Course 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
- Exploring advanced fine-tuning techniques and architectures for domain-specific LLM adaptation
- 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
- Exploring advanced architectures and techniques for LLM-powered recommender systems
- 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
- Implementing advanced evaluation and benchmarking techniques for LLM-based applications
- 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
- Implementing advanced techniques for mitigating biases and ensuring fairness in LLM-based applications
