Learning Roadmaps
AI, ML & Generative AI Learning Roadmaps
Each roadmap is a standalone, reviewed technical reference — not a course landing page. They cover what to learn, what to build, exit criteria and decision tables. Use them to orient, then choose a structured course when you are ready for guided implementation.
Choose by background
- Starting from zero? → AI Roadmap for Beginners
- Software engineer? → AI Developer Roadmap
- Want end-to-end systems? → AI Engineer Roadmap
- Analysis & modelling? → Data Science Roadmap
- Building LLM apps? → Generative AI Roadmap
- Building agents? → Agentic AI Roadmap
- Model-focused? → ML Engineer Roadmap
- Operating ML? → MLOps Roadmap
- Operating LLMs? → LLMOps Roadmap
- All production AI ops? → AIOps Roadmap
Foundations
Students
Artificial Intelligence Roadmap for Beginners
Start AI from zero — Python, data, maths, ML, DL and generative AI awareness.
Aspiring data scientists
Data Science Roadmap for Beginners and Working Professionals
Python, SQL, statistics, EDA, ML and communication — analysis and modelling first.
Engineering
Software engineers
AI Developer Roadmap 2026
Software-engineering-first AI application development: Python, RAG, tools, agents, MCP.
Engineers who want end-to-end AI system-building capability.
AI Engineer Roadmap
The broad engineering track: data, ML, DL, generative AI, serving and production systems.
Engineers focused on model development and handoff into MLOps.
Machine Learning Engineer Roadmap
Model-centric: data, features, evaluation, experimentation, reproducible training and serving fundamentals.
Applied AI
Engineers and practitioners building generative AI systems.
Generative AI Roadmap 2026
LLMs, prompt and context engineering, embeddings, RAG, multimodal, evaluation and deployment.
Engineers building reliable
Agentic AI Roadmap 2026
Production-first agentic systems: tools, state, memory, evaluation, security and deployment.
Operations
ML engineers
MLOps Roadmap 2026
Lifecycle-first: data contracts, reproducible training, serving, orchestration, monitoring and governance.
Engineers operating LLM and generative AI systems in production.
LLMOps Roadmap for Production AI Systems
LLM application lifecycle: prompt versioning, eval datasets, RAGOps, serving, cost control and guardrails.
Platform
AIOps Roadmap for Production AI Systems
Broad production AI operations: ModelOps, MLOps, LLMOps, PromptOps, RAGOps, AgentOps, reliability and governance.
Methodology
Roadmaps are reviewed when production practices, tools or platform patterns materially change. Each roadmap states its scope, exclusions, version and last review date. We do not fabricate market statistics, salaries or learner outcomes; technical claims cite primary sources. Commercial conversion happens through one contextual course bridge placed after the educational roadmap and project sections.