Get in Touch
 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

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories