Machine learning
Importance: Essential (5 of 5)
Understands core supervised, unsupervised, and deep-learning methods.
← All career guides8 of 8 skills26 of 41 courses
Data and AI
Machine Learning Engineer
Builds, deploys, scales, and operates machine-learning systems in production.
Skill coverage
100%
Course coverage
63%
Skill coverage shows how much of the role's capability map already has a published course. Course coverage shows how much of the mapped course pipeline for this role has shipped.
- Role skills
- 8
- Published coverage
- 8
- Relevant courses
- 26
Capability map
Skills
Importance describes how central each capability is to the role. Course coverage shows where you can build the skill in the current OpenSkills catalog.
01
Model development
4 capabilities
Feature engineering
Importance: Essential (5 of 5)
Produces reliable training and serving features from raw data.
Course coverage
- Feature EngineeringFree
- Feature Engineering for Time Series, Feature Stores with Feast, Streaming Feature Engineering (planned)
Model evaluation
Importance: Essential (5 of 5)
Measures model quality, robustness, bias, and failure modes.
ML frameworks
Importance: Very important (4 of 5)
Implements models with production-relevant training frameworks.
02
Production ML
4 capabilities
MLOps
Importance: Essential (5 of 5)
Makes training and release workflows reproducible and governed.
Course coverage
- MLOps Fundamentals
- Model Adaptation Strategies
- CI/CD for Machine Learning, Data and Model Versioning with DVC, Kubeflow Pipelines, MLflow for Experiment Tracking and Model Registry (planned)
Model serving
Importance: Essential (5 of 5)
Serves models with appropriate latency, scale, and reliability.
Course coverage
- Machine Learning Model Serving
- Batch Inference and Offline Scoring, GPU Architecture, KServe and Kubernetes Model Serving, NVIDIA Triton Inference Server, vLLM and LLM Inference Serving (planned)
Model monitoring
Importance: Essential (5 of 5)
Detects drift, degradation, and operational failures.
Course coverage
- Machine Learning Model Monitoring
- ML Incident Response and Alerting, Statistical Drift Detection Methods, ML Observability Tooling (planned)
Data pipelines
Importance: Very important (4 of 5)
Integrates reliable data processing with training and inference.
Learning path
Relevant courses
Core foundations
The concepts this role is built on.
Supporting knowledge
Adjacent knowledge that improves day-to-day judgment.
- Apache Airflow Fundamentals
- Artificial Intelligence Fundamentals
- Batch Data ProcessingFree
- Data Engineering Fundamentals
- Deep Learning Fundamentals
- Event Streaming
- Explainable AI
- Model Adaptation Strategies
- Neural Networks
- Recommender Systems
- Responsible AI
- Batch Inference and Offline Scoring, GPU Architecture, ML Incident Response and Alerting, Statistical Drift Detection Methods (planned)
Specializations
Deeper paths for particular environments or directions.
- Apache Spark Fundamentals
- Computer Vision
- Natural Language Processing
- PyTorch Fundamentals
- Reinforcement Learning
- TensorFlow Fundamentals
- Data and Model Versioning with DVC, Feature Engineering for Time Series, Feature Stores with Feast, KServe and Kubernetes Model Serving, Kubeflow Pipelines, ML Observability Tooling, MLflow for Experiment Tracking and Model Registry, NVIDIA Triton Inference Server, Streaming Feature Engineering, vLLM and LLM Inference Serving (planned)
Put the map to work
Find Machine Learning Engineer roles.
Search multiple job sources at once with queries tailored to this career, then use the skill map above to evaluate what each role actually asks for.