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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 3- or 4-day 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 3 or 4 days, 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: 40 Hours
- Runs During: Saturday & Sunday
Countdown
Course Outline
Morning Session: Introduction to SDLC
- What is SDLC?
- Definition and Purpose
- Phases of SDLC:
- Planning
- Analysis
- Design
- Implementation/Development
- Testing
- Deployment
- Maintenance
- SDLC Models:
- Waterfall
- Agile (Scrum, Kanban)
- V-Model
- Spiral Model
- DevOps
- Best Practices in SDLC
- Communication & Collaboration
- Documentation
- Version Control
- Code Reviews
- Continuous Integration/Continuous Delivery (CI/CD)
Afternoon Session: Introduction to Generative AI
- What is Generative AI?
- Definition and Concepts
- Types of Generative Models:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Transformer-based Models (e.g., GPT, BERT)
- Use Cases of Generative AI:
- Content Generation (text, images, videos, music)
- Code generation
- Data augmentation
- Generative AI in Software Development
- AI-assisted coding (e.g., GitHub Copilot, ChatGPT)
- Automated documentation generation
- Code optimization and refactoring using AI
- Hands-On:
- Introduction to GPT and its API (e.g., ChatGPT)
- How to generate code or documentation using AI tools
Day 2: Deep Dive into SDLC Phases & AI Integration
Morning Session: SDLC Phases in Detail
- Planning and Requirements Analysis
- Gathering and Analyzing Requirements
- Defining User Stories, Use Cases
- Feasibility Study (Technical, Operational, Financial)
- AI in Requirements Analysis (e.g., NLP for extracting user requirements)
- Design and Architecture
- High-Level Design vs. Low-Level Design
- UML Diagrams (Class, Sequence, Use Case)
- Design Patterns
- AI-assisted Design Tools (e.g., Sketching diagrams using AI)
- Hands-On:
- Designing a simple software system with Generative AI assistance for diagramming (e.g., using ChatGPT to generate UML diagrams)
Afternoon Session: AI in Development & Testing
- Development Phase
- Writing Code (Agile/Waterfall Implementation)
- Collaboration in Development (Git, Version Control)
- Code Generation using Generative AI
- How AI can assist in writing boilerplate code
- Refactoring and optimizing code using AI
- Testing Phase
- Types of Testing (Unit, Integration, System, Acceptance)
- Automated Testing Tools (JUnit, Selenium, etc.)
- AI for Testing (e.g., AI-powered test case generation, code coverage analysis)
- Bug Detection and Fixing using AI
- Hands-On:
- Code generation and optimization with AI tools
- Automated test case generation with AI (using tools like Test.ai or similar)
Morning Session: Advanced SDLC Topics
- Deployment & Continuous Delivery
- Deployment Pipelines and Automation
- CI/CD: Tools and Best Practices
- Handling Rollbacks, Hotfixes, and Versioning
- Maintenance and Support
- Post-Deployment Monitoring and Feedback Loops
- Bug Tracking and Issue Management
- Patch Releases and Updates
- AI for Post-Deployment
- AI in Application Monitoring (e.g., anomaly detection)
- Predictive Maintenance with AI
- Chatbots for customer support and issue resolution
Afternoon Session: AI-Driven Software Development
- AI in Agile Methodologies
- Sprint Planning and AI-powered task allocation
- AI for sprint retrospectives and continuous improvement
- AI-enhanced team collaboration tools
- Real-World Use Cases of AI in SDLC
- AI for Code Quality (e.g., Code Climate, DeepCode)
- AI-powered Software Documentation Tools
- Example Case Studies:
- Using AI for bug detection in large systems (e.g., Facebook’s AI-driven code analysis)
- Automating documentation generation with GPT models (e.g., Google’s AI for doc generation)
- Hands-On Project:
- Develop a simple application (e.g., a To-Do List) using AI-assisted code generation, testing, and deployment tools.
- Apply generative AI tools for documentation, testing, and deployment.
Implement a CI/CD pipeline with AI-based code review.
Morning Session: AI in Real-World Applications & SDLC Automation
- AI Integration in Large-Scale Projects
- Automating the SDLC using AI (e.g., AI for automated testing, integration)
- Smart Code Assistants in a Real-World DevOps Pipeline
- Data-Driven Decisions for Software Projects
- Enterprise-Level Use Cases
- AI in Large-Scale Applications (e.g., Fintech, Healthcare, E-Commerce)
- AI in DevOps and Cloud-Native Architecture (AI-driven microservices monitoring)
Afternoon Session: The Future of Generative AI & SDLC
- Emerging Trends in Generative AI
- Advancements in GPT, multimodal AI models
- AI for Predictive Analytics and Decision-Making
- The role of AI in Low-Code/No-Code Development
- Ethical and Security Considerations in AI
- Bias in AI models
- Ethical coding and responsible AI usage
- Security concerns with AI-generated code
- Closing Discussion & Q&A
- Open discussion on how attendees can integrate AI into their SDLC workflows
- Future of AI in software development and upcoming technologies
Optional Hands-On Project (for Day 4)
- Capstone Project:
- Work on a final group project where attendees implement a fully-fledged application using SDLC phases with integrated Generative AI tools.
- Demonstrate AI-assisted coding, testing, and deployment in a real-world scenario.
Presentations of group projects and feedback
- SDLC Tools: Jira, Trello (for task management), Git, Jenkins (CI/CD), Postman (API testing), Selenium (test automation)
- Generative AI Tools:OpenAI GPT API, Copilot, DeepCode, Test.ai
- Cloud Platforms: AWS, Azure (for deployment and DevOps)
- Documentation Tools:MkDocs, Sphinx, DocFX
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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
