Advanced Program Generative AI
Instructor-led training in Advanced Program Generative AI. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This advanced course dives deep into the theoretical underpinnings and practical applications of cutting-edge generative AI models. Participants will explore advanced architectures, fine-tuning techniques, and deployment strategies to build sophisticated AI solutions that create novel content, from text and code to images and more. Master the skills needed to innovate and implement the next generation of intelligent systems.
What you'll learn
- You will be able to analyze and compare advanced generative AI architectures, including large language models (LLMs) and diffusion models.
- You will be able to implement advanced fine-tuning and adaptation techniques for pre-trained generative models on custom datasets.
- You will be able to design and execute robust evaluation strategies for generative AI outputs, considering both quantitative and qualitative metrics.
- You will be able to apply ethical considerations and mitigate biases in the development and deployment of generative AI systems.
- You will be able to deploy and manage generative AI models in production environments using MLOps best practices.
- You will be able to develop advanced prompt engineering strategies to guide generative models for specific, complex tasks.
- You will be able to integrate generative AI capabilities into existing applications and workflows.
Curriculum
6 modules · outline is indicative and can be tailored to your team.
1Foundations of Advanced Generative AI
- Review of core generative AI concepts and models
- Deep dive into Transformer architectures and their variants
- Understanding Diffusion Models and their evolution
- Probabilistic graphical models and latent variable models in depth
2Advanced Model Architectures and Training
- Exploring advanced LLM architectures (e.g., MoE, Mixture of Experts)
- Generative Adversarial Networks (GANs) beyond basics
- Variational Autoencoders (VAEs) and their extensions
- Conditional generation and controllability in models
3Fine-tuning, Adaptation, and Prompt Engineering
- Parameter-Efficient Fine-Tuning (PEFT) methods (LoRA, QLoRA)
- Instruction-tuning and alignment techniques
- Advanced prompt engineering for complex reasoning and multi-turn interactions
- Retrieval-Augmented Generation (RAG) architectures
4Evaluation, Ethics, and Responsible AI
- Quantitative and qualitative evaluation metrics for generative models (e.g., BLEU, ROUGE, FID, human evaluation)
- Detecting and mitigating biases in generative outputs
- Ethical implications of synthetic content and deepfakes
- Privacy concerns and data governance in generative AI
5Deployment and MLOps for Generative AI
- Containerization and orchestration for generative models
- Serving large models efficiently (quantization, distillation)
- Monitoring and maintaining generative AI systems in production
- Scalability and cost optimization strategies
6Specialized Applications and Future Trends
- Generative AI for multimodal tasks (text-to-image, image-to-text, video generation)
- Code generation and intelligent assistants
- Generative AI in scientific discovery and drug design
- Introduction to agentic AI systems and autonomous agents
Prerequisites
Participants should have a strong understanding of machine learning fundamentals, deep learning concepts, and practical experience with Python programming and deep learning frameworks like TensorFlow or PyTorch.
Who should attend
This course is designed for experienced data scientists, machine learning engineers, AI researchers, and developers looking to specialize in advanced generative AI techniques and applications.
Certification
Frequently asked questions
What is the delivery format for this course?
This is an instructor-led, live-online course, providing real-time interaction with an expert instructor and fellow participants.
How long is this course?
This advanced course typically runs for 24-32 hours, usually spread across 3-4 full days or several weeks of shorter sessions.
Will I receive a certificate upon completion?
Yes, upon successful completion of the course, you will receive an Skilvi course-completion certificate.
Are exam vouchers included with the course?
Exam vouchers for relevant vendor certifications are available through authorized channels on request, but are not directly included in the course fee.
What are the technical requirements for this course?
You will need a reliable internet connection, a computer capable of running modern web browsers, and potentially access to cloud-based GPU resources for hands-on labs (details provided before class).
Is prior experience with generative AI required?
Yes, this is an advanced course. Participants are expected to have a foundational understanding of generative AI concepts and practical experience with deep learning.
Pricing on request
- Live online (VILT)
- 32–40 hours
- Hands-on labs & assignments
- Skilvi completion certificate
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