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Course Outline
Introduction to AI for Software Development for Government
- Differentiating Generative AI from Predictive AI
- Applications of AI in coding, analytics, and automation for government processes
- Overview of LLMs, transformers, and deep learning models relevant to public sector workflows
AI-Assisted Coding and Predictive Development for Government
- Utilizing AI-powered code completion and generation tools (e.g., GitHub Copilot, CodeGeeX) in government projects
- Predicting and mitigating code bugs and vulnerabilities before deployment in government systems
- Automating code reviews and providing optimization suggestions for government applications
Building Predictive Models for Software Applications for Government
- Understanding time-series forecasting and predictive analytics for public sector needs
- Implementing AI models for demand forecasting and anomaly detection in government services
- Utilizing Python, Scikit-learn, and TensorFlow for predictive modeling in government projects
Generative AI for Text, Code, and Image Generation for Government
- Working with GPT, LLaMA, and other LLMs to enhance government communications and documentation
- Generating synthetic data, text summaries, and official documentation using AI
- Creating AI-generated images and videos for public sector use cases
Deploying AI Models in Real-World Applications for Government
- Hosting AI models using platforms such as Hugging Face, AWS, and Google Cloud for government operations
- Building API-based AI services to support business applications within the public sector
- Fine-tuning pre-trained AI models for domain-specific tasks in government agencies
AI for Predictive Business Insights and Decision-Making for Government
- Leveraging AI-driven business intelligence and customer analytics for public sector initiatives
- Predicting market trends and consumer behavior to inform government policies
- Automating workflow optimizations with AI in government operations
Ethical AI and Best Practices in Development for Government
- Addressing ethical considerations in AI-assisted decision-making within the public sector
- Detecting and mitigating bias to ensure fairness in government AI models
- Implementing best practices for interpretable and responsible AI in government applications
Hands-On Workshops and Case Studies for Government
- Implementing predictive analytics for a real-world government dataset
- Building an AI-powered chatbot with text generation capabilities for public sector use
- Deploying an LLM-based application for automation in government workflows
Summary and Next Steps for Government
- Review of key takeaways from the AI training program for government professionals
- Resources and tools for further learning about AI applications in government
- Final Q&A session to address any remaining questions or concerns
Requirements
- A foundational knowledge of software development principles
- Practical experience with a programming language (Python is recommended)
- An understanding of machine learning or artificial intelligence basics (recommended but not required)
Audience for Government
- Software developers
- AI/ML engineers
- Technical team leaders
- Product managers with an interest in AI-driven applications
21 Hours
Testimonials (1)
Engagement, Extended Knowledge
