flyrank

AI-Powered Content Opportunity Scoring using Machine Learning | FlyRank ML Internship Capstone | Random Forest Regression | Search Intelligence

FlyRank Capstone: grounded personal agent with a Grounding Guard. Answers only from verified knowledge cards, cites sources, and refuses when evidence is missing.

Persistent Task Management REST API built with Node.js, Express.js, SQLite, and Swagger UI. Developed during the FlyRank Backend AI Engineering Internship using real-world educational workflows from Navigant Education Consultants.

A Machine Learning repository developed during my FlyRank AI Internship, focused on data engineering and model analytics.

Ranks anonymized search pages for refresh review. Random forest hits Precision@50 0.74 vs 0.24 baseline on client-holdout (30k pages). Decision-support queue with actions and reason codes.

FlyRank AI Backend AI Engineer task focused on running a Task API with a PostgreSQL database through Docker. The project containerizes the database and application, connects the API to PostgreSQL, and uses Docker Compose to start the complete stack with one command. It reinforces database integration, containerization, and modern deployment skills.

Capstone repo for the FlyRank Frontend AI Engineering internship — weekly deliverables from Frontend AI Engineering + AI Fluency tracks, evolving into a full capstone project.

BE-09: Visual AI Decision Flow using React Flow, Inngest, and OpenAI — a workflow editor where AI-powered YES/NO decisions dynamically control execution paths.[WEEK - 07]

My work and assignments for the FlyRank Machine Learning Internship, documenting weekly notebooks, experiments, and the capstone project.

A CRUD REST API built with Python and FastAPI, backed by SQLite for persistent storage. Same endpoints as the in-memory version — now tasks survive a server restart.

FlyRank ML Engineering Internship — Machine Learning Content Opportunity Scoring (Precision@50: 0.740 vs 0.340 Baseline)

Robust Python REST API built during the Flyrank Backend AI Engineer internship, featuring end-to-end Supabase Auth (signup, login, logout), cryptographic JWT verification, middleware-guarded protected routes, and interactive Swagger UI documentation for seamless testing and secure client integration. Engineered for production environments.

A CRUD REST API built with Python and FastAPI, backed by PostgreSQL running in Docker. Swaps in a Postgres repository behind the same service/route layer used in the in-memory and SQLite versions — full stack starts with one command: docker compose up.

Official portfolio, applied machine learning research paper, and autonomous decision-support agent for Sohila Khaled Abbas (Applied ML Intern & Search Intelligence Engineer at FlyRank AI | BI Developer).

My work for the FlyRank AI Machine Learning Internship — running the starter ML pipeline, notebooks and assignments week by week.

Machine Learning research project predicting declining web content using the FlyRank ML Internship dataset.

Repository for collected work at FlyRank Internship

Lightweight, persistent RESTful CRUD To-Do List API built with Python, FastAPI, SQLModel, and SQLite. Features automatic database schema creation, pre-seeding, Pydantic input validation, and Swagger UI docs. FlyRank Backend Track (BE-01 & BE-02).

Mission-critical Python LLM endpoint built during the Flyrank Backend AI Engineer internship. Transforms unstructured input into schema-validated JSON with production safeguards: strict execution timeouts, exponential-backoff retries, persistent token-cost audit logging, and an instant circuit-breaker kill switch for reliable AI deployment.

CRUD Task API — evolved from in-memory storage to SQLite to a containerized PostgreSQL stack with Docker Compose, Swagger docs, and staged commits.