Foundation
ML foundations
- Python & ML concepts
- Supervised / unsupervised learning
- Deep learning basics
- Model evaluation
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Build the practical foundations to develop, evaluate and productionize machine-learning systems. Progress from core ML concepts to deployment, MLOps and a role-relevant cloud ML credential.
Each layer builds on the one before it — foundation to credential.
Follow the four levels in order, but you do not need to take every course. Core courses form the essential path; Recommended courses add role-based depth. Planned gap-fill courses are clearly marked Coming soon.
Build the essential concepts and vocabulary needed before moving into applied practitioner work.
Machine LearningBuild the essential concepts and vocabulary needed before moving into applied practitioner work.
Core SkillBuild a clear working foundation in the concepts, terminology, risks and practical context of the topic.
Core SkillBuild the essential concepts and vocabulary needed before moving into applied practitioner work.
Core SkillDesign and apply retrieval-augmented generation patterns with attention to quality, scale and production use.
RAGMove models and AI applications into reliable production with deployment, monitoring and lifecycle practices.
MLOpsBuild a clear working foundation in the concepts, terminology, risks and practical context of the topic.
Agentic AIBuild a clear working foundation in the concepts, terminology, risks and practical context of the topic.
Core SkillBuild a clear working foundation in the concepts, terminology, risks and practical context of the topic.
RiskBuild a clear working foundation in the concepts, terminology, risks and practical context of the topic.
Core SkillApply the topic through practical workflows, controls, assessments and real-world practitioner scenarios.
Core SkillBuild, evaluate and tune practical ML models in Python using an end-to-end workflow.
Core SkillDevelop advanced capability to design, lead and operationalize this area in enterprise environments.
Core SkillTake AI applications from prototype through testing, deployment, monitoring, security and production operations.
Core SkillOperate LLM and agentic systems in production with evaluation, observability, versioning, cost and reliability controls.
Agentic AIPrepare for a recognized credential through structured review, applied scenarios and exam-focused practice.
Core SkillPrepare learners to build, operationalize, deploy and monitor ML workloads for the current AWS ML Engineer Associate exam.
Machine LearningPrepare learners to design, build, productionize and optimize ML solutions on Google Cloud.
Cloud Security✓ A curated Core path so you know what to learn first.
✓ Recommended depth without making every course mandatory.
✓ Competency-based progression from foundation to mastery.
✓ A clear credential destination and role outcome.
Get the complete live roadmap in one bundle. New courses added to this roadmap later can be included as the path evolves.
13 live courses, foundation through credential.
Launch your Graphy