Course Modules:
Module 1: Introduction to AI Deployment
The AI lifecycle: from training to production
What is MLOps and why it matters
Key challenges in operationalizing AI
Module 2: Tools and Environments
Setting up Jupyter, Google Colab, and VSCode
Introduction to GitHub, Docker, and Conda environments
Local vs. cloud workflows
Module 3: Building APIs for AI Models
Using Flask or FastAPI to serve models
Creating REST endpoints for prediction
JSON request/response structure
Module 4: Containerization with Docker
Dockerizing your AI application
Building and running containers
Deploying containers locally or on the cloud
Module 5: Deploying on Cloud Platforms
Overview of AWS, Azure, and Google Cloud for AI
Deploying with Google Cloud Run / AWS Lambda
Using AI services (SageMaker, Vertex AI, etc.)
Module 6: Monitoring and Model Management
Logging and monitoring predictions
Versioning and retraining models
Data drift detection and alerts
Module 7: Automating Workflows with MLOps
CI/CD pipelines for ML
Using MLflow or DVC for experiment tracking
Scheduling with Airflow or Prefect
Module 8: Final Project – Deploy a Full AI Pipeline
Choose one:
Deploy a sentiment analysis API
Create a computer vision model served as a web app
Launch a forecasting model to the cloud
Submit Docker container, code repo, and live URL
Tools & Technologies Used:
Flask / FastAPI
Docker
Git, GitHub
AWS, Google Cloud, Azure
MLflow, DVC, Airflow (optional)
Target Audience:
Intermediate AI/ML practitioners
Developers who want to bring AI into production
Data scientists aiming for full-stack AI roles
Engineers automating intelligent systems
Global Learning Benefits:
Learn to turn AI models into production-ready applications
Master modern tools for model deployment and automation
Build a portfolio of deployable AI projects
Understand how to scale, manage, and maintain AI systems over time
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