AI EngineeringCurriculum in development
ML & Deep Learning Engineer
Develop data, training, evaluation, and serving workflows for machine-learning and deep-learning models.
Designed forSoftware, data, and analytics professionals moving into model development.
Recommended foundationPython, basic statistics, linear algebra fundamentals, and confidence working with tabular data.
Problems you will learn to solve
Work from real constraints, not generic tool demonstrations.
Build reproducible training data
Compare models using the right metrics
Move a model from notebook to a monitored service
Working environment
Python, PyTorch, scikit-learn, Pandas, MLflow, FastAPI, Docker, cloud
System mapA model development lifecycle
Training data
Experiments
Feature pipeline
Model training
Evaluation
Model registry
Serving + monitoring
Provisional curriculum
Six connected modules
The sequence will be validated with practitioners before enrollment opens.
- 01Data and statistical foundations
- 02Classical machine learning
- 03Neural-network foundations
- 04Training and experiment design
- 05Evaluation and error analysis
- 06Serving and monitoring
Planned capstone
Finish with evidence of applied skill.
A reproducible model pipeline with training, experiment tracking, evaluation, deployment, and monitoring.
Focused lessonsUnderstand the underlying ideas
Guided practiceWork through realistic constraints
Applied projectProduce a demonstrable result
Structured reviewRevise the work after feedback
Program updates
Register your interest in ML & Deep Learning Engineer.
This is not enrollment and no payment is required. We will use your response to validate demand and contact you when the curriculum and cohort details are ready.