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The “Gen AI for Backend Developers” course teaches you on how to integrate AI into your backend workflows. From code generation to the performance tuning, this course will turn complex backend work into efficient, AI-assisted tasks. Whether you’re writing APIs or debugging legacy code, AI can assist you in that.
This course is packed with hands-on modules focused on real-world backend problems and how AI can solve them faster. You will dive into tools like GitHub Copilot, explore AI-powered bug fixing, automate documentation, and even optimize your databases using machine intelligence. We don’t just teach theory, you will be able to apply AI across the backend lifecycle: code, test, deploy, and maintain. By the end of this course you will walk away with practical experience and confidence to use Gen AI effectively in your current or future projects.
With Gen AI you will be able to Generate clean, reusable backend code
Auto-refactor legacy codebases with smart suggestions
Create and document APIs faster with AI tools
Optimize databases and backend systems for peak performance
Automate deployments and system health monitoring with AI
Topics: Using AI tools like GitHub Copilot, Tabnine
Subtopics: Generating reusable code snippets
Learning Objective: Improve backend code efficiency and maintainability
Outcome: Developers write better, optimized code with AI assistance
Use Case: AI-generated backend boilerplate code for an enterprise system
Assessment: Coding challenges
Topics: AI-powered debugging tools and automated fixes
Subtopics: Refactoring old codebases for efficiency
Learning Objective: Reduce debugging time and enhance code quality
Outcome: Lower defect rates in backend applications
Use Case: Refactoring legacy enterprise software using AI
Assessment: AI-assisted debugging exercises
Topics: Generating API documentation and endpoints with AI
Subtopics: AI-assisted API design and implementation
Learning Objective: Speed up API development and improve documentation
Outcome: Faster and more efficient API development
Use Case: AI-powered API documentation and generation
Assessment: Hands-on API development assignment
Topics: AI-driven database indexing and query optimization
Subtopics: AI recommendations for indexing and query restructuring
Learning Objective: Enhance database performance and reduce query time
Outcome: Improved database performance with lower query execution time
Use Case: Optimizing a slow-performing database using AI
Assessment: Database query optimization task
Topics: Load balancing and backend tuning using AI
Subtopics: Predicting traffic spikes and allocating resources
Learning Objective: Improve backend system stability and scalability
Outcome: Highly scalable backend with AI-driven optimizations
Use Case: Load balancing an e-commerce backend using AI
Assessment: Backend performance testing with AI tools
Topics: AI-optimized build automation and deployment pipelines
Subtopics: Automating testing and rollback mechanisms
Learning Objective: Automate CI/CD processes for faster deployments
Outcome: Continuous integration with minimal manual intervention
Use Case: Setting up an AI-driven CI/CD pipeline for a SaaS product
Assessment: CI/CD automation test
Topics: Using AI to detect failures before they occur
Subtopics: Predictive analysis for backend system failures
Learning Objective: Prevent downtime and optimize server health using AI
Outcome: Proactive system maintenance and failure prevention
Use Case: Monitoring and predicting system failures using AI
Assessment: Final project: Full AI-driven backend deployment
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No. It’s designed for those with backend development experience and some DevOps understanding.
Yes, all learning resources and session recordings will be available to you for lifetime access.
We focus on AI tools like GitHub Copilot, Tabnine, and AI integrations for CI/CD, debugging, and performance monitoring.
Yes. Each module includes real-world projects and assessments to apply your learning.
No. This course uses continuous assessment through projects and practical assignments.
You can redo any assignment until you meet the expected outcome with mentor support.
Absolutely. The skills taught here are highly relevant for AI-integrated backend roles.
Yes, and we encourage it! The certificate helps establish your credibility in Gen AI applications.
Basic knowledge of backend development and some exposure to DevOps.
Complete the full course and projects. Once approved, you’ll receive the official certification.
It refers to using AI to generate or optimize backend tasks like coding, testing, or deployment.
Kind of, but more advanced. This course dives deeper into backend use-cases with specialized tools and workflows.
