Curated for this specific career level.
Level path
ML Practitioner
MLE
Own features and services with solid implementation judgment across neighboring layers.
Concepts explicitly listed in this level.
Concepts span multiple tracks and layers.
Job description signalsTypical responsibilities↓
- Productionize models, feature pipelines, training workflows, serving systems, and monitoring loops.
- Partner with data scientists and product teams to turn experiments into reliable ML/AI capabilities.
- Improve model quality, evaluation, scalability, latency, cost, and operational safety.
- Own features end to end from design through implementation, testing, release, and support.
- Make local technical tradeoffs and explain them clearly to teammates.
- Improve reliability, maintainability, and observability of the systems you touch.
- Collaborate with product, design, QA, operations, and adjacent engineering teams.
Track / layer masteryConcept coverage by layer↓
Data
15 concepts
Theory
1 concepts
Software
121 concepts
Data
33 concepts
Management
16 concepts
Operations
12 concepts
Product
8 concepts
Theory
4 concepts
Learning scopeConcepts by learning area↓
Specific to this level
Concepts introduced by this level
Level concepts
ML Foundations
Understand the major forms of learning and the basic model-building workflow.
Level concepts
Neural Networks & Deep Learning
Learn how neural networks are trained and why they power modern AI systems.
Level concepts
AI Products
Connect ML models to useful product behavior.
Level concepts
Ml Systems
Build the capabilities needed for this area.
Cumulative level expectation
All concepts expected at this level
Inherited path
Coding Foundations
Developers begin by learning to translate requirements into working, readable code.
Inherited path
Developer Tooling
Daily development depends on version control, language tooling, static checks, and repeatable local workflows.
Inherited path
Testing & Debugging
A developer owns not only writing code, but proving that it behaves as intended.
Inherited path
Employment Basics
Technical candidates benefit from understanding who employs them, how they are paid, and which terms govern the working relationship.
Inherited path
Programming Foundations
Learn the basic building blocks used to express behavior in code.
Inherited path
Software Design
Structure code so it remains readable, testable, and maintainable as it grows.
Inherited path
Data & Persistence
Understand how applications store, retrieve, and protect durable state.
Inherited path
Architecture & Systems
Design systems that handle growth, latency, failure, and multiple communicating components.
Inherited path
Production Awareness
Know how software is built, deployed, observed, and recovered in production.
Inherited path
ML Foundations
Understand the major forms of learning and the basic model-building workflow.
Inherited path
Neural Networks & Deep Learning
Learn how neural networks are trained and why they power modern AI systems.
Inherited path
AI Products
Connect ML models to useful product behavior.
Inherited path
ML Production
Deploy, monitor, and iterate on ML systems in real environments.
Inherited path
Application Design
Mid-level developers own complete features and make local design choices inside an established architecture.
Inherited path
Runtime & Data
Feature ownership requires practical knowledge of runtimes, memory management, and relational persistence.
Inherited path
Delivery & Collaboration
Professional development includes the complete implementation loop, not only editing source files.
Path concepts
Ml Systems
Build the capabilities needed for this area.