Machine Learning Fundamentals
The concepts every ML question assumes: training, evaluation metrics, overfitting, and knowing when not to use machine learning.
AI & Machine Learning
25 topics explaining what each cloud service does, why it exists, and which certification tests it. Written in our own words — we explain concepts rather than paraphrasing documentation.
Model fundamentals, the ML platform and generative AI.
The concepts every ML question assumes: training, evaluation metrics, overfitting, and knowing when not to use machine learning.
Training, serving, pipelines and monitoring in one place — the MLOps surface that production machine learning needs.
Foundation models, prompting, grounding, retrieval augmentation and evaluation — and choosing the right technique for a failure.