AI Engineering: Build & Deploy ML Models
Build Real ML Systems — From Prototype to Production
Not another intro to Python. This is a practitioner’s course on the full ML engineering lifecycle — data pipelines, model training, evaluation, serving, and monitoring in real-world production environments.
Enroll now – $697$697 · One-time · Lifetime accessWhat you will build and carry forward
This course includes: 14 hours of technical instruction covering ML project structure, feature engineering, model training and evaluation, API serving with FastAPI, containerisation with Docker, and production monitoring.
Outcomes
- Structure an ML project that scales from experiment to production without a rewrite
- Engineer features from raw data using reproducible, version-controlled pipelines
- Train, evaluate, and iterate on models using rigorous evaluation frameworks
- Serve models as production APIs using FastAPI and Docker
- Monitor model performance and detect drift before it becomes a problem
- Deploy to cloud infrastructure (AWS/GCP) with CI/CD pipelines
How the course is structured
Version control, experiment tracking, and reproducibility from day one.
Feature engineering, transformation, and validation at scale.
Hyperparameter tuning, cross-validation, and experiment management.
Metrics, confusion matrices, and the tests that actually matter.
FastAPI, request validation, and async inference endpoints.
Docker, CI/CD, drift detection, and the on-call mindset.
Who this course is for
ML engineers, data scientists, and senior developers ready to take models from notebook to production.
Enroll now – $697$697 · One-time · Lifetime accessSecure checkout · Lifetime access · One-time payment