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Gen AI - SDLC Course
Learn to use the combination of Generative AI Software Development Life Cycle (Gen AI SDLC) refers to the systematic process of developing software that incorporates generative AI capabilities. The stages of the Gen AI SDLC adapt traditional SDLC processes to account for the complexities of AI systems, such as training models, data management, and ethical considerations.
- 4.8 Ratings / 1200 Reviews
- 4000+ Professionals Enrolled
- 97.3% Success Rate
- 20 Assignments
- Case Study
- Final Assessment
- Certification
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Course Overview
General Description:
Creating a training program that combines Generative AI (Gen AI) and the Software Development Life Cycle (SDLC) requires a structured approach to cover the fundamentals, advanced topics, and practical applications. Below is a detailed outline that spans 16 Hrs, balancing theory, hands-on sessions, and real-world case studies
Course Objectives
Gain foundational knowledge of generative AI technologies, including models like GANs, transformers, and diffusion models.
Learn about the role and challenges of generative AI in the software development life cycle (SDLC).
Target Audience / Prerequisites
To ensure participants are well-prepared and can fully benefit from the course, the following prerequisites are recommended:
- Familiarity with AI concepts, such as supervised and unsupervised learning, deep learning, and neural networks.
- Understanding of common AI frameworks (e.g., TensorFlow, PyTorch, or Keras).
- Proficiency in Python, especially in libraries like NumPy, pandas, and scikit-learn.
- Basic understanding of coding practices for software development (e.g., version control with Git).
- Basic understanding of the traditional software development life cycle (SDLC) stages (e.g., requirements, design, development, testing, deployment).
- Software Developers and Engineers
- Technology Leaders and Managers
- AI/ML Professionals
- DevOps and MLOps Practitioners
- Data Analysts and Data Engineers
- Software Developers
Certification
Sample Certificate
- After successfully completing the training programme, as well as the real-time hands-on and projects, you will be awarded a Futuretech course completion certificate.
Course Duration & Course Schedule Date
- Duration: 16 Hours
- Runs During: Saturday & Sunday
Countdown
Course Outline
Module 1: Introduction
• Overview of Generative AI and its impact on software development
• Understanding different types of GenAI models
• Key GenAI platforms and their capabilities
• Ethics and responsible use of GenAI in development
Module 2: Requirements Phase
Tools and Techniques
• Using ChatGPT/Claude for requirement analysis and refinement
• GitHub Copilot for user story generation
• Requirements validation using GenAI
Hands-on Activities
• Converting business requirements to user stories
• Generating acceptance criteria
• Identifying edge cases using AI
Module 3: Design Phase
Tools and Techniques
• AI-assisted system architecture design
• Database schema generation using GenAI
• API design assistance
• UI/UX prototyping with AI tools
Hands-on Activities
• Generating ERD diagrams using Mermaid and AI
• Creating API specifications
• Design pattern recommendation systems
Module 4: Development Phase - Part 1
Tools and Techniques
• GitHub Copilot for code generation
• Amazon CodeWhisperer basics
• Tabnine for code completion
• AI-assisted code documentation
Hands-on Activities
• Setting up AI coding assistants
• Practice exercises with pair programming
• Code documentation generation
Module 5: Development Phase - Part 2
Tools and Techniques
• Unit test generation using AI
• Code refactoring with AI assistance
• Debug assistance using GenAI
Hands-on Activities
• Generating unit tests
• Refactoring complex code
• Debugging exercises
Module 6: Testing Phase
Tools and Techniques
• Test case generation using GenAI
• AI-powered test automation
• Performance testing assistance
Hands-on Activities
• Generating test cases from requirements
• Creating test data
• Writing test scripts with AI assistance
Module 7: Deployment & Maintenance
Tools and Techniques
• CI/CD pipeline optimization with AI
• Infrastructure as Code (IaC) assistance
• Log analysis and monitoring
• AI-assisted incident response
Hands-on Activities
• Generating deployment scripts
• Creating monitoring dashboards
• Incident analysis practice
Module 8: Best Practices & Conclusion
• Integration strategies for AI tools in development workflow
• Cost considerations and ROI analysis
• Security considerations when using GenAI
• Future trends in AI-assisted development
• Course summary and Q&A
Assessment Methods
• Hands-on projects throughout the course
• Kahoot Quizzes after each module
Prerequisites
• Basic understanding of programming concepts
• Familiarity with any programming language
• Access to required AI tools and platforms
Resources
• Access to OpenAI/Claude API
• GitHub Copilot subscription
• IDE with AI integration capabilities
• Course materials and documentation
Reviews
Job Opportunities
FAQ
The Software Development Life Cycle (SDLC) is a systematic process used to develop software applications, ensuring high-quality results and meeting customer requirements. It consists of distinct stages, including planning, analysis, design, implementation, testing, deployment, and maintenance.
It ensures a structured approach, improving quality, reducing risks, and meeting deadlines.
Examples include Jira (planning), GitHub (development), Selenium (testing), and Jenkins (deployment). one can do it smoothly.
- Changing requirements
- Time and cost underestimation
- Communication gaps
.
- Clear roadmap
- Risk management
- Better quality and maintainability
Gen AI - SDLC Course
₹7,999.00
Schedule Starts from Jan 2025
(4th Weekend)
( Weekend Classes )
Special Offer for limited seats only… Hurry Up!!!
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Gen AI - SDLC Course
₹7,999.00
Schedule Starts from Jan 2025
( Weekend Classes )
Special Offer for limited seats only… Hurry Up!!!
