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Full Stack AI Developer Roadmap
·
Module 11
MLOps and Model Deployment
1
Dockerizing ML Applications
Containerize ML applications using Docker
2
Tracking Experiments and Models with MLflow
Track experiments and model versions using MLflow
FastAPI Model Deployment
Deploy models as REST APIs using FastAPI
4
Asynchronous Request Handling in FastAPI
Implement asynchronous request handling in FastAPI
5
GitHub Actions for CI/CD
Create CI/CD workflows with GitHub Actions for testing and deployment
6
Cloud Deployment for Containerized Applications
Deploy containerized applications to cloud platforms (AWS/GCP basics)
7
Production Model Monitoring
Monitor model performance metrics in production
8
Statistical Data Drift Detection
Detect data drift using statistical methods
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Large Language Models and Generative AI
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Full Stack AI Application Development