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Course Outline
Introduction to Artificial Intelligence in Software Development
- Distinguishing between Generative and Predictive AI technologies
- Applications of AI in coding, data analytics, and process automation for government operations
- Overview of Large Language Models (LLMs), transformer architectures, and deep learning frameworks
AI-Assisted Coding and Predictive Development Lifecycle
- Utilizing AI-powered code completion and generation tools (e.g., GitHub Copilot, CodeGeeX) to enhance developer productivity
- Predicting software defects and security vulnerabilities prior to deployment
- Automating code reviews and providing optimization recommendations for secure system maintenance
Developing Predictive Models for Software Applications
- Principles of time-series forecasting and predictive analytics in public sector contexts
- Implementing AI models for resource demand forecasting and anomaly detection
- Applying Python, Scikit-learn, and TensorFlow for robust predictive modeling for government
Generative AI for Document, Code, and Media Generation
- Leveraging GPT, LLaMA, and other Large Language Models for content creation
- Generating synthetic datasets, executive summaries, and technical documentation
- Utilizing diffusion models to create AI-generated images and videos for communication purposes
Deploying AI Models in Production Environments
- Hosting and managing AI models via Hugging Face, AWS, and Google Cloud platforms
- Developing API-based AI services to support agency mission requirements
- Fine-tuning pre-trained AI models for specialized, domain-specific tasks
AI-Driven Predictive Business Insights and Strategic Decision-Making
- Utilizing AI-driven business intelligence and customer analytics for improved service delivery
- Forecasting market trends and constituent behavior to inform policy decisions
- Automating workflow optimizations through intelligent process management
Ethical AI Considerations and Development Best Practices
- Addressing ethical implications in AI-assisted decision-making processes
- Ensuring bias detection, fairness, and equity in AI model outcomes for government
- Adhering to best practices for interpretable, transparent, and responsible AI deployment
Hands-On Workshops and Case Studies
- Implementing predictive analytics using real-world government datasets
- Constructing an AI-powered chatbot with natural language generation capabilities
- Deploying an LLM-based application to streamline administrative automation
Summary and Strategic Next Steps
- Review of key takeaways and strategic insights
- Overview of AI tools and educational resources for continued professional development
- Final question and answer session
Requirements
- Knowledge of foundational software development principles
- Practical experience with programming languages (Python is suggested)
- Awareness of machine learning or artificial intelligence concepts (recommended, though not mandatory)
Target Audience
- Software developers
- AI/ML engineers
- Technical team leaders
- Product managers focused on AI-enabled applications
21 Hours
Testimonials (2)
use of proper and effective prompt
Marses Pacaldo
Course - Generative AI and Prompt Engineering for Corporate Professionals
The interactive style, the exercises