Hi Recruiter, let me walk you through This sample projects called Sentiment Analysis API Platform.
The goal of this project is to analyze customer text and determine whether the sentiment is:
- Positive
- Negative
- Neutral
The solution is exposed as a REST API and can be integrated with applications such as:
- Customer feedback systems
- Product review platforms
- Social media monitoring tools
- Customer support analytics
Organizations receive large amounts of customer feedback every day.
Manually analyzing this feedback is:
- Time-consuming
- Expensive
- Not scalable
The objective of this solution is to automate sentiment classification and provide quick insights for business teams.
Although this is a technical project, I would approach it as a Technical Project Manager by:
- Managing project delivery
- Coordinating development and deployment activities
- Tracking sprint progress
- Managing stakeholders
- Coordinating releases
- Monitoring production health
- Managing risks and dependencies
The overall flow is:
Client Application
↓
FastAPI REST API
↓
Machine Learning Model
↓
Sentiment Prediction
↓
JSON Response
The API receives text input.
The machine learning model processes the text and predicts sentiment.
The result is returned back as a JSON response.
Backend:
- Python
- FastAPI
Machine Learning:
- Sentiment Analysis Model
Containerization:
- Docker
Cloud:
- AWS EC2
Monitoring:
- Prometheus
- Grafana
Project Management Tools:
- Jira
- Confluence
Sample Input:
"I really love this product. The experience was amazing."
Expected Output:
{ "sentiment": "Positive" }
Another Example:
"The service was terrible and very disappointing."
Expected Output:
{ "sentiment": "Negative" }
This demonstrates how the API can analyze customer feedback in real time.
To ensure consistency across environments:
- The application is packaged inside Docker containers.
- Developers can run the same application locally.
- QA can validate the same build.
- Production runs the same image.
Benefits:
- Faster deployment
- Environment consistency
- Easier scaling
The application can be deployed on AWS EC2.
Deployment Flow:
Developer Code
↓
Git Repository
↓
Build Process
↓
Docker Image
↓
AWS EC2 Deployment
↓
Production Environment
Benefits:
- Faster releases
- Repeatable deployments
- Reduced deployment issues
Monitoring is critical in production systems.
This project uses:
Used for:
- Collecting metrics
- Monitoring API performance
- Tracking request counts
- Tracking error rates
Used for:
- Dashboards
- Visualization
- Performance monitoring
- Operational reporting
Key Metrics:
- API response time
- Request volume
- Error percentage
- System health
Some common risks include:
- Infrastructure failures
- Model accuracy degradation
- Deployment failures
- Unexpected traffic spikes
- Production incidents
Mitigation:
- Monitoring alerts
- Rollback strategy
- Release validation
- Performance testing
- Regular health checks
If this were delivered in a Scrum model:
Sprint 1:
- Requirement gathering
- Architecture design
Sprint 2:
- API development
Sprint 3:
- Dockerization
Sprint 4:
- AWS deployment
Sprint 5:
- Monitoring setup
Sprint 6:
- UAT and production release
Throughout the project:
- Daily standups
- Sprint planning
- Sprint reviews
- Retrospectives
- Stakeholder demos
If usage grows:
I would consider:
- Load balancing
- Multiple API instances
- Container orchestration using Kubernetes
- Auto-scaling
- Centralized monitoring
This ensures the platform can support increasing traffic and business growth.
The final solution provides:
- Automated sentiment analysis
- REST API integration capability
- Cloud deployment readiness
- Monitoring and observability
- Scalable architecture
From a Technical Project Manager perspective, this project demonstrates:
- Technical understanding
- Cloud deployment knowledge
- DevOps awareness
- Monitoring strategy
- Agile delivery management
- End-to-end ownership
=======================================
Production-ready Sentiment Analysis API with FastAPI, Docker, AWS, Prometheus & Grafana — showcasing end-to-end MLOps deployment and monitoring.
This project demonstrates a production-ready Sentiment Analysis API built with FastAPI, containerized using Docker, deployed on AWS EC2, and monitored using Prometheus and Grafana. It showcases real-world MLOps practices including model serving, cloud deployment, and observability.
- Python (FastAPI, scikit-learn)
- Docker (Dockerfile, docker-compose)
- AWS EC2 (Ubuntu instance)
- Prometheus (metrics scraping)
- Grafana (dashboard visualization)
- GitHub (version control)
- Developed a simple ML model (sentiment analysis).
- Built API layer using FastAPI.
- Containerized the application with Docker.
- Deployed the containerized app on AWS EC2.
- Configured Security Groups for API access.
- Integrated Prometheus for metrics collection.
- Set up Grafana dashboards for visualization.
- Tested API endpoints with curl/Postman.
- Documented architecture and deployment steps.
This project highlights end-to-end skills in:
- Machine Learning model serving
- API development
- Containerization
- Cloud deployment
- Monitoring & observability
It is designed to showcase MLOps and DevOps capabilities in real-world scenarios.
Shows request rate, error rate, response time, and status codes.
Highlights 95th percentile latency, top endpoints, and request types.
Displays error rate over time, uptime, and CPU usage.
git clone https://github.com/<your-username>/sentiments-api-project.git
cd sentiments-api-project
docker-compose up -d
---
Access:
API → http://<EC2-IP>:8000
Prometheus → http://<EC2-IP>:9090
Grafana → http://<EC2-IP>:3000