BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!
Course Overview
This five day, instructor led Generative AI Engineering course in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online, 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. This course is intended for Programmers, Software Engineers, Computer Scientists Data Scientists, Data Engineers and Data Analysts. At the completion of this course, participants will be able to:
- 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
Schedule
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 301-258-8200.
Prerequisites
All learners are required to have:
- Extensive prior Python development experience
- Core Python Data Science skills, including the use of NumPy and Pandas
- Inferential statistics
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
BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!
Phoenix TS is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints re-garding registered sponsors may be submitted to the National Registry of CPE Sponsors through its web site: www.nasbaregistry.org
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