AI Model Deployment: From Prototype to Production
web-development.
Course Modules:
Module 1: Understanding Deployment in the AI Lifecycle
From model training to model serving
Challenges in model deployment
Key concepts: inference, latency, scalability
Module 2: Creating APIs for AI Models
Introduction to Flask and FastAPI
Building REST endpoints for predictions
Sending and receiving JSON data
Module 3: Docker for Model Packaging
What is Docker and why use it for AI
Writing Dockerfiles for Python projects
Running and testing Docker containers
Module 4: Deploying on Cloud Platforms
Google Cloud Run and AWS Lambda
Deploying models with Heroku or Azure App Services
Managing cloud environments and costs
Module 5: Model Versioning and Rollouts
Handling model updates and rollback
Canary deployments and A/B testing
Integrating CI/CD pipelines
Module 6: Monitoring and Performance
Logging prediction results
Tracking inference time and resource usage
Setting alerts and usage limits
Module 7: Final Project – Deploy Your Own Model
Choose a use case (e.g., sentiment analysis, image classification, fraud detection)
Build and deploy a complete API or web app
Submit your GitHub repo, live demo link, and deployment report
Tools & Technologies Used:
Python, Flask, FastAPI
Docker
Google Cloud, AWS, Heroku
GitHub, Postman, JSON, REST
Target Audience:
AI/ML learners ready for production environments
Python developers transitioning to AI deployment
Data scientists aiming to build end-to-end solutions
Anyone seeking full-stack AI skills
Global Learning Benefits:
Turn AI prototypes into deployable applications
Master tools used by modern AI and DevOps teams
Build and showcase projects that employers can test live
Prepare for roles in AI engineering, MLOps, and software deployment
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