Master Study AI

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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