Applied Machine Learning Engineering
Learn the full applied ML lifecycle, from raw data to a deployed and monitored service, with a hands-on program for learners with basic Python.
Taught by Dr. Henry Kang · Lead Instructor · AI & Machine Learning Engineering
Applied Machine Learning Engineering
with Dr. Henry Kang
WEEKS
4
WEEKLY EFFORT
8–10 hours
CATEGORY
ML Engineering
FORMAT
Live Online
PRICE
$2200
Work through the complete applied machine learning lifecycle: problem framing, data collection, cleaning and exploratory analysis, leakage-safe feature engineering, supervised and unsupervised modeling, rigorous evaluation, and reproducible pipelines. You will deploy a model as a containerized FastAPI service with Docker, then add monitoring, drift detection, and cost controls.
Responsible AI practices, including bias analysis, privacy, and model cards, are integrated throughout. The toolchain includes Python, scikit-learn, pandas, PyTorch, Docker, cloud or serverless deployment, and MLflow. Assessment includes a midterm quiz, a written final, graded labs, and a deployable capstone.
The four-week program is taught in English and includes two two-hour live sessions each week plus an approximately two-hour asynchronous lab per session. Grading is Pass/Fail, with 70% required to pass.
What you'll learn
- Build reproducible data-to-model pipelines
- Select and evaluate models rigorously
- Perform leakage-safe feature engineering
- Deploy a containerized model API
- Add monitoring, drift detection, and model cards
- Apply responsible-AI safeguards throughout the lifecycle
Prerequisites
- Basic Python proficiency
- Docker installed locally
- Free-tier cloud access
- Free GitHub and Colab accounts
Certificate of Completion
Students who complete all course requirements receive a verified digital certificate issued by Abryne University.
Earn a Certificate of Completion
Students who complete all course requirements receive a verified digital certificate from Abryne University — shareable on LinkedIn and your resume.