6 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.
How to become an AI engineer? Skill set, a step-by-step roadmap, portfolio and production project building, finding a job in Türkiye, and career transitions in one guide.
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.
A concrete roadmap to land a global remote AI Engineer position from zero in 12 months: 5 production-level projects, GitHub portfolio + blog strategy, $200K+ offer. Karpathy, Raschka, 3Blue1Brown, Andrew Ng curriculum; HuggingFace + LangChain + Anthropic Academy free programs; Turkish alternatives; case study (14-month timeline); and interview strategy for top offers.
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.
One of the biggest mistakes in enterprise generative AI initiatives is moving quickly into technology without asking the right strategic questions first. In reality, many failed projects do not fail because the model is weak, but because the use case is vague, the data is not ready, the success metrics are wrong, ownership is unclear, risk management is delayed, and scaling realities are ignored. Before launching a generative AI initiative, the right questions often matter more than the model choice itself. This guide presents 20 critical strategic questions that enterprises should answer before starting a generative AI project, covering business value, data, security, operations, cost, governance, human oversight, and scaling.