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


4 days


  • 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

  • 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
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
  • 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