Sentiment Analysis: End-to-End ML Pipeline with Cloud-Native Deployment

PythonFastAPIMLflowDVCDockerKubernetesGrafanaPrometheusMLOpsCloud DeploymentNLP
Sentiment Analysis: End-to-End ML Pipeline with Cloud-Native Deployment
  • A production-grade machine learning system for sentiment analysis, featuring automated data ingestion, preprocessing, model training, experiment tracking, and scalable cloud deployment.
  • This project implements a full MLOps lifecycle for sentiment analysis using Python and FastAPI. It includes an automated data pipeline for ingestion and preprocessing, experiment tracking using MLflow, and containerized deployment with Docker and Kubernetes.
  • Integrated cloud storage for model versioning and Grafana + Prometheus monitoring for real-time system performance.
  • Implements CI/CD workflows for continuous delivery and model retraining automation, enabling production-ready deployment pipelines for NLP workloads.