PROJECT ID: [ TDC_AIML ]
AI & MACHINE LEARNING
MISSION MANIFESTO
Stop learning AI. Start building it.
An end-to-end machine learning platform built for real-world deployment — engineering intelligent pipelines that span data ingestion, feature extraction, model training, and high-speed inference. Designed to tackle computer vision, predictive modelling, and spatial awareness at scale, the system is optimised for low-latency performance in dynamic, production environments. Built to eliminate the gap between experimentation and deployment — so models don't just train well, they ship fast and hold up under pressure.
PROJECT HIERARCHY
SYSTEM STACK
Core Languages
Python/SQL
ML
Scikit-Learn
Data
Pandas/NumPy/Matplotlib
Backend
FastAPI/PostgreSQL
Frontend
React
Computer Vision
OpenCV
Deep Learning
TensorFlow/Keras/PyTorch/ONNX Runtime
DevOps
Docker
MLOps
MLflow/Kubeflow/Metaflow
Pipelines/Orchestration
Apache Airflow/Prefect/Dagster
PROJECT TIMELINE
Set Up & Scope In
Stack configured, team met, problem defined. Day 5 — you know exactly what you're building
Raw data → clean pipeline
Real datasets. Real mess. You handle it — noise, gaps, and all.
Make the data speak
EDA — visualise patterns, catch anomalies, shape your model strategy.
First model. First results.
Train, evaluate, iterate. Learn what the metrics are really telling you.
Push the model harder
Tune hyperparameters, try deeper architectures. Move the needle — meaningfully.
Model meets the world
Live FastAPI endpoint. Real inference. Your model now responds to requests.
Give it a face
React dashboard — clean, live, and actually useful. AI output people can see.
Demo day. Ship it.
Docker packaged, documented, presented. You leave with working code — and a story.
ARE YOU READY TO BUILD?
Submit your application for AI & MACHINE LEARNING. The project lead will review your profile and tech stack before approving access.