4 posts
How to build an AI roadmap? A 12-month phased plan: foundation, first use case, expansion and scale phases, gate criteria for each phase, and realistic milestones.
What is an AI roadmap? An AI roadmap is a strategic document where an organization, starting from its business goals, prioritizes AI use cases, measures its current state with a maturity model, and usually crystallizes it as a 12-month plan. This guide: a clear definition, why it is needed, how to build it, the maturity model, use-case prioritization, its relation to AI strategy, common mistakes, and FAQs.
Deep comparison of 3 main learning paths for AI/ML engineering candidates + students: LeetCode (algorithm focus, Big Tech interview), Kaggle (competition + ML algorithm + Notebooks tier), Real Project (end-to-end, GitHub portfolio, production experience). Each path strengths, time investment, job-finding contribution, position-priority matching, Turkish company vs US Big Tech vs European differences, hybrid strategy recommendations, Junior vs Senior focus difference, 12-month recommended mix, time-investment ROI calculation, 8 success stories, common mistakes, post-interview feedback distribution.
Detailed 12-month roadmap to become an AI engineer from zero: Month 1-2 Python + math foundation, Month 3-4 classic ML, Month 5-6 deep learning + PyTorch, Month 7-8 LLM + RAG + agentic, Month 9-10 MLOps + production, Month 11-12 specialized + job search. Each month with specific courses (Coursera, fast.ai, DeepLearning.AI), books, milestone projects, Turkish resources (BTK Akademi, Coursera Turkish subtitles), daily study plan, portfolio requirements (5-10 GitHub projects), Kaggle strategy, certifications, job application tactics. SMB/freelance/abroad options.