Machine Learning Engineering
Build, train, evaluate, and deploy ML models in production. Scikit-learn, PyTorch, feature engineering, MLOps pipelines. This architect track is structured for adult learners who need practical, career-relevant depth without academic abstraction. Delivered as a live experience, the course combines guided milestones, implementation reviews, and applied exercises aligned with modern AI, engineering, and technical leadership work.
Price
$6,000
Tax included
Duration
12 weeks
Format
Live
Instructor
Elena Novak
Build, train, evaluate, and deploy ML models in production. Scikit-learn, PyTorch, feature engineering, MLOps pipelines.
Course Plan
Syllabus
Frame applied ML projects with production constraints and success metrics in mind.
- Problem framing
- Data pipelines
- Evaluation baselines
Outcomes
Includes
- • 12 live weeks with model labs, code reviews, and deployment clinics.
- • Feature engineering and experiment tracking templates.
- • Scikit-learn and PyTorch workflow examples.
- • Production ML case studies covering quality, drift, and release risk.
Instructor
Elena Novak
Elena is an ML systems engineer who bridges experimentation and production delivery across applied machine learning teams.