1. What is AI Deployment?
AI deployment is the process of taking a trained model and integrating it into production systems so it can deliver real-world value. This involves scaling, monitoring, and maintaining AI models responsibly.
2. Deployment Steps
- Model Testing: Validate accuracy, fairness, and robustness.
- Containerization: Use Docker or similar tools for portability.
- API Integration: Expose model functionality through APIs for applications.
- Monitoring: Track performance, drift, and errors in real-time.
- Scaling: Use cloud services like AWS, Azure, or GCP for large workloads.
- Security & Ethics: Ensure data privacy and ethical use.
3. Deployment Example
Scenario: Deploy a sentiment analysis model as a web service.
Steps:
Steps:
- Train a sentiment model using Python and scikit-learn.
- Save the model using joblib.
- Create a Flask API to serve the model.
- Containerize with Docker.
- Deploy on cloud service (AWS ECS or Azure App Service).
4. Recommended Tools & Platforms
- Docker & Kubernetes — containerization and orchestration
- Flask/FastAPI — create lightweight APIs
- AWS Sagemaker / Azure ML / GCP AI Platform — managed deployment
- Prometheus & Grafana — monitoring and visualization
5. Exercise
Pick a small AI model (like text classification or image recognition). Try creating a simple API endpoint using Flask or FastAPI, then test it locally. Observe the response time and behavior.
6. Inspirational Quote
"Deployment is where ideas meet impact — delivering intelligence to the world." — AI Free Learning