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● CAREER ROADMAP
AI/ML Engineering
Build models and AI features that ship real value.
AI/ML Engineers build, deploy and monitor models and AI-powered features. The field is booming, but the winning skill is shipping useful, responsible ML — not just training models. A strong portfolio matters more than a PhD.
The complete AI/ML Engineering field roadmap - everything worth learning, from fundamentals to advanced. Highlighted core topics are the ones we take you through in the UstackSchool programme.
Core - taught in the programmeRecommendedOptional / advanced
1
Programming & Maths
PythonNumPy & PandasLinear algebraStatisticsProbabilityCalculus basics
2
Data Prep & EDA
Data cleaningFeature engineeringVisualisation (matplotlib / seaborn)Data pipelines
3
Classic Machine Learning
scikit-learnRegressionClassificationModel evaluationClusteringEnsembles (XGBoost)Cross-validationFeature selection
4
Deep Learning
Neural networksPyTorchBackpropagationCNNsTensorFlow / KerasRNNs / LSTMOptimization
5
Computer Vision
Image classificationObject detectionSegmentationOpenCV
6
NLP & LLMs
Tokenization & embeddingsTransformersHugging FacePrompt engineeringRAGFine-tuningVector databases
7
MLOps
Model deployment (FastAPI)DockerExperiment tracking (MLflow)Model monitoringCI/CD for MLFeature stores
8
Cloud & Scale
GPU trainingAWS SageMakerGCP Vertex AIDistributed training
9
Data Engineering for ML
SQLData pipelinesSpark
10
Responsible AI
Bias & fairnessExplainability (SHAP)Model governance
11
Get job-ready
End-to-end ML capstoneKaggle competitionsPortfolioInterview prep
Tools & technologies
Pythonscikit-learnPyTorch / TensorFlowpandas & NumPyJupyterMLflowDockerCloud ML services
Ready to start the AI/ML Engineering path? Get the skills, portfolio and support to actually land the role.