Data Science & AI
Machine Learning Engineering
Move beyond notebooks: build, deploy, and maintain machine learning systems in production.
- Duration
- 20 weeks
- Format
- Online Live
- Level
- Intermediate
- Credential
- Professional Certificate
Program overview
This program is built for practitioners who can train a model but want to master everything that surrounds it: data pipelines, deployment, monitoring, and MLOps. You will take models from experiment to production, learning the engineering discipline that separates ML prototypes from ML products.
What you'll be able to do
- Design reliable training and inference pipelines
- Package and deploy models as APIs and batch services
- Implement experiment tracking, model registries, and CI/CD for ML
- Monitor models for drift and performance degradation in production
- Reason about cost, latency, and scaling trade-offs in ML systems
Tools & technologies
- Python
- scikit-learn
- PyTorch
- Docker
- FastAPI
- MLflow
- Airflow
- AWS SageMaker
- GitHub Actions
Curriculum
5 modules · 20 weeks · every module ends with reviewed applied work.
ML Engineering Foundations
- From notebook to package
- Testing ML code
- Reproducibility and environments
- Project structure for ML teams
Data & Training Pipelines
- Pipeline orchestration with Airflow
- Data validation and contracts
- Feature engineering at scale
- Experiment tracking with MLflow
Serving & Deployment
- Model APIs with FastAPI
- Containerization with Docker
- Batch vs real-time inference
- Cloud deployment patterns
MLOps & Reliability
- CI/CD for machine learning
- Model registries and versioning
- Monitoring, drift, and alerting
- Rollbacks and shadow deployments
Capstone: Production ML System
- Design review with instructors
- Build a full training-to-serving system
- Load testing and observability
- Final presentation and code review
Projects & capstone
You graduate with evidence, not just knowledge. Key projects include:
Real-time fraud-scoring API with monitoring dashboards
Automated retraining pipeline with drift detection
Capstone: a production ML service you design, build, and defend
Who this program is for
- Data scientists moving toward engineering
- Software engineers entering ML
- ML practitioners formalizing MLOps skills
Eligibility
Working knowledge of Python and basic machine learning (equivalent to our Data Science Professional Certificate). A skills check is provided before enrollment.
Your instructors
Daniel Osei
Principal Instructor, Machine Learning Engineering
Daniel built and operated ML platforms serving millions of daily predictions at scale. He teaches machine learning as an engineering discipline — reproducible, tested, monitored — and mentors students through the messy realities of production systems.
Dr. Mei-Lin Chang
Faculty, Deep Learning & AI Research
A former research scientist in computer vision and NLP, Mei-Lin has published in leading AI venues and now translates frontier research into teachable, buildable curriculum. Her paper-reading seminars are a student favorite.
Career opportunities
Roles this program prepares you to pursue:
- Machine Learning Engineer
- MLOps Engineer
- AI Platform Engineer
- Data Engineer (ML-focused)
Earn a verifiable certificate
Complete all required project work to earn the Pioneer Academy Machine Learning Engineering certificate — digitally issued with a unique verification link for your CV and LinkedIn.
Program FAQs
Is this program hands-on?
Extremely. Roughly 70% of your time is spent building: pipelines, APIs, deployments, and monitoring. Lectures exist to support the labs, not the other way around.
Which cloud provider do you use?
Core labs use AWS with patterns that transfer directly to GCP and Azure. We focus on concepts and portable tooling rather than vendor lock-in.
Take the next step
Start your Data & AI journey
The next Machine Learning Engineering cohort is enrolling now. Applications take under ten minutes, and seats are confirmed in order of application.