Building Real-Time ML Systems: APIs, Models, and Deployment

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Learn the complete machine learning lifecycle the way it actually happens in industry—through one cohesive, real-world project: building a real-time Urban Air Quality Index (AQI) prediction system. Starting from a blank repo, you'll scope the business problem, then collect data from government AQI APIs, OpenWeatherMap, and web-scraped traffic and industrial sources using scheduled, fault-tolerant ingestion scripts. You'll clean messy multi-source sensor data, engineer powerful temporal, weather, and geospatial features, and build a reproducible pipeline versioned with DVC. From there, you'll train and tune multiple models (Random Forest, XGBoost, LightGBM) with time-aware cross-validation, track every experiment in MLflow, and explain predictions with SHAP. Finally, you'll ship it: package the pipeline, serve it through a FastAPI REST endpoint, build an interactive map-based Streamlit dashboard, containerize with Docker, deploy to the cloud via CI/CD, and set up drift detection and automated retraining with Evidently AI. Across 4 modules and 42 focused videos, you'll finish with a production-grade, portfolio-ready ML system running end-to-end. Independent Course Disclaimer Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

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