WA3309
Generative AI Engineering Training
This Generative AI Engineering course teaches students the applications and the techniques used to develop and engineer these systems. Attendees learn how to build and evaluate Generative AI models for a variety of tasks such as text generation, image synthesis, and music composition.
Course Details
Duration
5 days
Skills Gained
- Understand the basics of generative AI and its applications
- Learn about different techniques and algorithms used in generative AI
- Develop skills to design and implement generative AI models
- Gain proficiency in evaluating and optimizing generative AI models
- Apply generative AI models to real-world problems
Prerequisites
- Extensive prior Python development experience
- Core Python Data Science skills, including the use of NumPy and Pandas
- Inferential statistics
Target Audience
- Programmers
- Software Engineers
- Computer Scientists
- Data Scientists
- Data Engineers
- Data Analysts
Course Outline
- Introduction to Generative AI
- Generateive AI’s Roots in Machine Learning
- Understanding Generative models
- Contrasting Generative and Discriminative Models
- The original LLM models – from BERT to GPT
- Current Cloud- and Offline-Based LLM’s
- Generative AI Architecture
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GAN)
- Reinforcement Learning from Human Feedback (RLHF)
- Transformers
- Generative Pre-Trained Transformers (GPT)
- Tuning Generative AI Models
- Building Generative AI Models
- How Pre-Training Works
- Data Preparation and Preprocessing
- Fine Tuning Generative AI Models
- Formatting Data for LLM Fine Tuning
- Fine Tuning GPT
- Transfer learning Techniques
- Evaluation and Optimization of Generative AI Models
- Evaluating model performance
- Common evaluation metrics for generative AI models
- Building Generative AI Applications (part 1)
- Application Design Building Blocks
- Use Cases of LLM Based Applications
- Prompt Engineering Basics
- Prompt Templates
- RAG with Llama Index
- Case Studies and Real-World Applications
- Generative AI for Text
- Generative AI for Media
- Generative AI for Code
- Building Generative AI Applications (part 2)
- Customizing with Prompt Engineering
- Advanced Prompt Types
- Customizing with RAG
- Customizing with SYSTEM/CONTEXT Arguments and Prompt Templates
- Customizing with Fine Tuning
- Design Considerations and Tradeoffs for Customizing
- Tying It Together with LangChain
- ChatBots
- Chat Bot Basics
- Building LLM-Based Chat Bots
- Security
- Security Risks with Generative AI
- Secure Software Development
- Connectivity
- Exploitation of AI Systems (Jailbreaks)
- Infrastructure Concerns
- System Vulnerabilities
- Data Privacy and Leaks
- Malicious Use of AI
- Obscuring Data for Privacy and Security
- Best Practices for Security with Generative AI in Enterprises
- Future Directions in Generative AI Products and Model Development
- Best Practices, Limitations, other Considerations
- Future of Work
- Future Evolution of Gen AI
Upcoming Course Dates
