AI-Powered Software Development Certification Specialization. This course explores how to use generative AI across all phases of the software development lifecycle to automate coding, testing, planning, and deploying applications. This specialization provides a fully functional and practical path to applying generative AI throughout the software development lifecycle (SDLC). Learners in this course learn how to automate key project phases from requirements gathering and project planning to design, coding, testing, and deployment using tools such as GitHub Copilot, ChatGPT, and Hugging Face Transformers. This course teaches participants to generate multilingual requirements, design software architecture, refactor legacy code, and improve the product testing and release cycle. It also explores ethical issues in AI to ensure responsible implementation. During this course, learners will learn how to implement AI-based workflows and leverage emerging technologies to streamline complex engineering processes. Upon completion of the program, individuals will be able to automate development tasks, process unstructured data for documentation and information extraction, and evaluate AI outputs for quality, security, and compliance. Additionally, they will learn how to manage privacy and reduce bias in AI systems. The course includes real-world projects. The first project automates the deployment of a Node.js application on AWS EC2 using GitHub Copilot, Terraform, and Kubernetes. The second project focuses on infrastructure optimization and performance monitoring with Prometheus and GitHub Copilot. The third project uses ChatGPT for project planning, risk assessment, and risk management, giving learners a comprehensive experience in cloud automation and AI-based project management.

What you will learn

• Building AI Workflows: Create productive AI-based workflows using GitHub Copilot and ChatGPT.
• Automate development stages: Automate the stages of software planning, coding, testing, and deployment.
• Data processing and localization: Processing unstructured data for documentation, information extraction, and text localization.
• Evaluation of AI outputs: Assessing the quality, security, and ethical compliance of AI outputs.
• Running Practical Projects: Implementing infrastructure automation projects with Terraform, Kubernetes, and Prometheus.

This course is suitable for people who:

• Software developers: Programmers who intend to implement generative AI tools in their everyday coding.
• Test and Quality Engineers: QA engineers who want to automate software testing and evaluation processes.
• Technology professionals and project managers: People looking to integrate generative AI into real-world projects and improve project management.