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⁕ Technical Depth
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.
Technical14 hours · Self-pacedLifetime access
$697 · One-time · Lifetime access
⁕ The Curriculum
What 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.
- ⁕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
⁕ The Curriculum Arc
How the course is structured
01
ML Project Structure
Version control, experiment tracking, and reproducibility from day one.
02
Data Pipelines
Feature engineering, transformation, and validation at scale.
03
Model Training
Hyperparameter tuning, cross-validation, and experiment management.
04
Evaluation Frameworks
Metrics, confusion matrices, and the tests that actually matter.
05
API Serving
FastAPI, request validation, and async inference endpoints.
06
Production & Monitoring
Docker, CI/CD, drift detection, and the on-call mindset.
⁕ Who This Is For
⁕
ML engineers, data scientists, and senior developers ready to take models from notebook to production.
Begin a considered
practice.
One course, your pace, lifetime access.
$697One-time · Lifetime access
⁕ Secure checkout · Lifetime access · No subscription required for single course