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Designing and Implementing Enterprise-Grade ML Applications

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...

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Course Code WA3518
Duration 4 days
Available Formats Classroom

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.

Skills Gained

  • Implement advanced fine-tuning and quantization techniques for domain-specific LLM adaptation and efficient deployment
  • Design and implement LLM-powered recommender systems using hybrid architectures and evaluation techniques
  • Apply advanced model evaluation, interpretation, and debugging techniques for understanding and improving LLM behavior
  • Implement ethical considerations and responsible AI practices for mitigating biases and ensuring fairness in enterprise-grade LLM applications

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)

Course Details

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