Designing and Implementing Enterprise-Grade ML Applications


Course Number: AI-140WA
Duration: 4 days (26 hours)
Format: Live, hands-on

Machine Learning Training Overview

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.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

  • 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

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)

Outline

Expand All | Collapse All

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

Training Materials

All Generative AI training students receive comprehensive courseware.

Software Requirements

All attendees must have a modern web browser and an Internet connection.



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