Course Outline
Introduction to AI in Software Development
- Distinguishing between Generative AI and Predictive AI
- The role of AI in coding, analytics, and automation
- An overview of LLMs, transformers, and deep learning architectures
AI-Assisted Coding and Predictive Development
- Leveraging AI for code completion and generation (e.g., GitHub Copilot, CodeGeeX)
- Identifying code bugs and vulnerabilities pre-deployment
- Automating code reviews and providing optimisation insights
Constructing Predictive Models for Software
- Exploring time-series forecasting and predictive analytics techniques
- Deploying AI models for demand forecasting and anomaly detection
- Utilising Python, Scikit-learn, and TensorFlow for predictive modelling
Generative AI for Text, Code, and Visual Content
- Working with GPT, LLaMA, and other Large Language Models
- Generating synthetic data, text summaries, and technical documentation
- Creating AI-generated images and videos using diffusion models
Deploying AI Models in Production Environments
- Hosting AI models via Hugging Face, AWS, and Google Cloud
- Developing API-based AI services for business use cases
- Fine-tuning pre-trained AI models for specific domain tasks
AI for Predictive Business Intelligence and Decision Support
- Enhancing business intelligence and customer analytics with AI
- Forecasting market trends and consumer behaviour patterns
- Optimising workflows through AI automation
Ethical AI and Best Practices in Development
- Navigating ethical considerations in AI-assisted decision-making
- Detecting bias and ensuring fairness in AI models
- Adopting best practices for interpretable and responsible AI
Practical Workshops and Case Studies
- Applying predictive analytics to a real-world dataset
- Building an AI-powered chatbot with text generation capabilities
- Deploying an LLM-based application for automation tasks
Summary and Future Pathways
- Recap of key learning outcomes
- Recommended AI tools and resources for continued development
- Final Q&A session
Requirements
- A solid grasp of fundamental software development concepts
- Proficiency in any programming language (Python is preferred)
- Knowledge of machine learning or AI basics (advisable but not mandatory)
Target Audience
- Software developers
- AI/ML engineers
- Technical team leads
- Product managers keen on developing AI-driven solutions
Testimonials (3)
Comprehensiveness about the backround; how to do a prompt.
Olaf Kannt - Helios Klinikum Krefeld
Course - Generative AI and Prompt Engineering in Healthcare
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
