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Duration 21 hours
Course Outline
Foundations of Artificial Intelligence in Software Engineering
- Differentiating between generative and predictive artificial intelligence frameworks
- Strategic application of AI in code synthesis, data analytics, and process automation
- Technical overview of Large Language Models (LLMs), transformer architectures, and deep learning mechanisms
AI-Enhanced Coding and Predictive Development Practices
- Leveraging AI-driven code completion and generation tools (e.g., GitHub Copilot, CodeGeeX)
- Proactively identifying code defects and security vulnerabilities prior to deployment
- Streamlining code review processes and generating optimization recommendations
Developing Predictive Models for Software Applications
- Analyzing time-series forecasting methodologies and predictive analytics techniques
- Deploying AI models for demand forecasting and anomaly detection systems
- Utilizing Python, Scikit-learn, and TensorFlow for robust predictive modeling
Generative AI for Text, Code, and Visual Media Creation
- Operationalizing GPT, LLaMA, and other large-scale language models
- Producing synthetic datasets, automated text summaries, and technical documentation
- Synthesizing AI-generated visual and video content using diffusion models
Deploying AI Models in Operational Environments
- Hosting AI solutions via Hugging Face, AWS, and Google Cloud platforms
- Constructing API-integrated AI services for enterprise business applications
- Adapting pre-trained AI models for specialized domain-specific tasks
AI for Strategic Business Intelligence and Decision Support
- Implementing AI-driven business intelligence and customer analytics capabilities
- Forecasting market trends and analyzing consumer behavior patterns
- Optimizing operational workflows through AI-driven automation
Ethical Governance and Best Practices in AI Development
- Addressing ethical implications in AI-assisted decision-making processes
- Detecting bias and ensuring fairness within AI model structures
- Establishing best practices for interpretable and responsible AI governance
Practical Workshops and Case Studies
- Applying predictive analytics to real-world operational datasets
- Developing AI-powered communication interfaces with text generation capabilities
- Deploying LLM-based applications for automated operational tasks
Concluding Review and Strategic Next Steps
- Synthesizing key learning outcomes and operational takeaways
- Identifying AI tools and resources for continued professional development
- Facilitating final inquiries and strategic discussions
Requirements
- Demonstrated understanding of fundamental software development principles
- Proficiency in at least one programming language (Python is preferred)
- Knowledge of machine learning or AI fundamentals (recommended but not mandatory)
Target Audience
- Software engineers and developers
- AI/ML specialists and architects
- Technical program leads and managers
- Product managers focused on AI-enabled public sector applications
Testimonials (2)
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