What I learned: Machine Learning Foundations
Notes on AWS Academy Graduate — Machine Learning Foundations: core ideas, practical skills, and the Credly link.
In short
Foundational ML on AWS—data, training vs inference, model types at a high level, and how cloud resources support experiments—so generative AI and MLOps topics later sit on solid ground.
The credential
AWS Academy Graduate — Machine Learning Foundations — Training Badge. Issuer: Amazon Web Services Training and Certification. Verify on Credly.
What the course is for
This track is aimed at people who need shared language across data science, engineering, and operations: what a model is, how data quality limits outcomes, and where compute and storage fit in the story.
Foundations themes
- Data first — labeling, splits, leakage, and why “more data” is not always the answer.
- Training vs serving — why the cost profile and risk profile change after deployment.
- Common model families — intuition without deriving every equation.
- AWS building blocks — how storage, compute, and managed ML services work together in typical labs.
Skills I took away
- Asking clearer questions about baseline metrics and acceptance criteria before choosing a model.
- Discussing pipelines (prepare → train → evaluate → deploy) with both devs and stakeholders.
- Spotting where platform defaults (IAM, networking, quotas) affect ML workloads.
Related posts
Neural Networks in Depth explains how neural nets learn (forward pass, backpropagation, architectures). Generative AI Foundations builds on this vocabulary. Data Engineering covers the upstream data path on AWS.