Overview
As the demand for AI-driven solutions continues to soar, staying ahead requires expertise in generative platforms. This program allows you to explore the power of large language models (LLMs) and craft custom applications whilst exploring different techniques like Stable Diffusion. You'll leave this program equipped with the tools and knowledge needed to thrive in the age of generative AI.
This Generative AI for Software Developers course will be delivered to you in partnership with Udacity, meaning you’ll have access to both Udacity’s learning and career services as well as RMIT Online’s course enablement support through our Learner Success Team.
Recognition
After completing an RMIT Future Skills course, you will earn an RMIT credential which can be validated, recognised and shared on social media platforms.
Content
By the end of this course, you’ll be able to:
- Situate generative AI within the broader history, context, and applications of artificial intelligence and deep learning
- Adapt generative foundation models to perform tasks in novel contexts
- Use LLMs and prompt engineering to create a custom chatbot
- Use image generation models such as Stable Diffusion to perform image inpainting
- Build applications that use LLMs, implement semantic search with vector database, and apply retrieval augmented generation techniques
How does it work ?
During this course, you'll have the opportunity to demonstrate your newly acquired Generative AI for Software Developer skills through a series of projects.
In the first project, you explore advanced techniques in AI and apply lightweight fine-tuning to a Foundation Model. In the second you will build a custom chatbot. For your third, you will use the Segment Anything Model (SAM) and Stable Diffusion to replace parts of images with AI-generated content. Finally, you will build a "real estate agent" application that uses LLMs for content generation, vector databases, semantic search and RAG techniques to transform standard real estate listings into personalised narratives.
