🤖 AI / ML Engineer

Fast-track roadmap: build, deploy, and maintain machine-learning systems.

Community / Career Guidance / AI / ML Engineer

Who this is for

Developers who want to combine software engineering with machine learning and put models into real products - not just notebooks.

Phase 1 · Foundations

Strong Python and ML basics

  • Be fluent in Python, NumPy, and pandas.
  • Understand core ML: train/test, overfitting, evaluation metrics.
  • Build classic models with scikit-learn.
Pythonscikit-learnML basics
Phase 2 · Deep Learning

Neural networks

  • Learn how neural networks train (loss, gradients, epochs).
  • Use a deep-learning framework to build simple networks.
  • Understand where deep learning helps and where it does not.
Neural netsFrameworksGPU basics
Phase 3 · Shipping Models

Deployment and APIs

  • Wrap a model in a REST API.
  • Package it with containers so it runs anywhere.
  • Version your data and models.
REST APIsDockerModel versioning
Phase 4 · MLOps

Run models reliably

  • Monitor accuracy and detect data drift.
  • Automate retraining and testing pipelines.
  • Track experiments so results are reproducible.
MonitoringPipelinesExperiment tracking
Phase 5 · Specialise

Pick a depth: NLP, CV, or LLMs

  • Go deep in one area - language, vision, or generative AI.
  • Build a production-grade project end to end.
  • Learn to use LLMs and agents responsibly.
NLPComputer visionLLMs

Learn it with Learn2Pro

The Python AI Bootcamp takes you through Phases 1-2, and the Agentic AI Bootcamp introduces modern LLM and agent workflows.

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