Developer career roadmap

Machine Learning Engineer Roadmap

Build, test, package, deploy, and observe machine-learning systems and deep-learning workloads.

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Dependency-aware sequence

1
Programming Foundations
Understand how programs process data, make decisions, repeat work, organize logic, and report errors, then build and debug a command-line application.
2
Python Fundamentals
Build, test, and debug useful Python programs with clear control flow, reusable functions, structured data, files, exceptions, packages, and classes.
3
Data Structures and Algorithms
Select, implement, analyze, and test common data structures and algorithms using practical constraints rather than memorized patterns.
4
SQL and Relational Databases
Design a relational schema, write reliable queries, update data safely, use transactions, and diagnose common correctness and performance problems.
5
Statistics for Data Science
Summarize data, reason about probability and sampling, interpret confidence intervals and tests, and communicate statistical uncertainty responsibly.
6
Data Analysis with Python
Clean, explore, summarize, validate, combine, and communicate findings from structured datasets through reproducible Python workflows.
7
Machine Learning Fundamentals
Prepare data, train baseline regression and classification models, evaluate predictions, detect leakage and overfitting, and document limitations.
8
Deep Learning Fundamentals
Build, train, evaluate, diagnose, and document neural networks while understanding optimization, representation, reproducibility, and deployment constraints.

Project ladder

Stage 1

Build evidence that combines the skills from the roadmap stages you have completed.

Stage 2

Build evidence that combines the skills from the roadmap stages you have completed.

Stage 3

Build evidence that combines the skills from the roadmap stages you have completed.

Extend your capability

Design useful tests, separate unit and integration concerns, test error behavior, recognize unreliable tests, and create a balanced test strategy.

Build, run, secure, debug, connect, persist, test, and distribute containerized applications and multi-service development environments.

Design, deploy, secure, monitor, scale, govern, and estimate the cost of a small cloud-hosted system using portable architecture concepts.

Build secure, testable, versioned, observable, and recoverable pipelines for software integration, artifacts, releases, deployments, and rollback.