Generative AI Training for Data Scientists Overview
This Intermediate Generative AI (GenAI) course is for Machine Learning and Data Science professionals who want to learn advanced GenAI and LLM techniques like fine-tuning, domain adaptation, and evaluation. Participants learn how to leverage popular tools and frameworks, including Python, Hugging Face Transformers, and open-source 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
- Master advanced fine-tuning techniques for LLMs to improve the quality and relevance of outputs
- Implement Retrieval Augmented Generation (RAG) to enhance LLM capabilities with real-time information retrieval
- Utilize vector embeddings for semantic search, recommendations, and other NLP applications
- Optimize LLM performance with techniques like quantization and pruning, ensuring efficient deployment and serving
- Address ethical considerations and adhere to best practices in AI development, mitigating bias, ensuring transparency, and safeguarding privacy
Prerequisites
- Practical experience (6+ months) minimum in Python - functions, loops, control flow
- Data Science basics - NumPy, pandas, scikit-learn
- Solid understanding of machine learning concepts and algorithms
- Regression, Classification, Unsupervised learning (clustering, Neural Networks)
- Strong foundations in probability, statistics, and linear algebra
- Practical experience with at least one deep learning framework (e.g., TensorFlow or PyTorch) recommended
- Familiarity with natural language processing (NLP) concepts and techniques, such as text preprocessing, word embeddings, and language models
Outline
Expand All | Collapse All
Introduction
Advanced Fine-Tuning and RAG Techniques
- Advanced fine-tuning techniques for LLMs
- Implementing Retrieval Augmented Generation (RAG)
- Improving LLM output quality and relevance
- Building a RAG-powered LLM application for a specific use case
Vector Embeddings and Semantic Search
- Introduction to vector embeddings and their applications in NLP
- Using vector embeddings for semantic search and recommendation systems
- Generating vector embeddings from text data
- Implementing a similarity search using libraries like Faiss or Annoy
LLM Optimization and Efficiency
- Techniques for optimizing LLM performance
- Applying optimization techniques to reduce LLM model size and inference time
- Strategies for efficient deployment and serving of LLMs in production
Ethical Considerations and Best Practices
- Addressing biases and fairness issues in LLMs
- Ensuring transparency and accountability in LLM-powered applications
- Best practices for responsible AI development and deployment
- Navigating privacy and security concerns when working with LLMs and sensitive data
Conclusion
Training Materials
All Generative AI training students receive comprehensive courseware.
Software Requirements
All attendees must have a modern web browser and an Internet connection.
