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Full Stack AI Developer Roadmap
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Module 12
Full Stack AI Application Development
1
Designing AI Application Architecture
Design system architecture for full stack AI applications
2
Streamlit for Interactive AI Apps
Build interactive AI application interfaces using Streamlit
Deploying ML Models as API Endpoints
Integrate ML models with backend APIs and create prediction endpoints
Background Job Processing with Celery and Redis
Configure task queues (Celery/Redis) for background job processing
Delegating Long-Running ML Tasks to Workers
Implement endpoints that delegate long-running ML tasks to workers
6
File Uploads and Asynchronous AI Processing
Handle file uploads and trigger async AI processing pipelines
7
Feedback for Model Improvement
Collect user feedback on predictions for model improvement
8
Input Validation & Prompt Injection Prevention
Apply security best practices: input validation and prompt injection prevention
End of course,
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