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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

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