fast.ai Alternative:
From ML Theory to LLM Jobs.

fast.ai is an excellent free resource created by Jeremy Howard, and the practical approach to teaching deep learning is genuinely valuable. However, three gaps affect career changers: 1) The curriculum focuses on traditional ML and deep learning, not the LLM engineering skills dominating 2026 job postings. 2) Self-paced with no accountability means most learners stall out before becoming job-ready. 3) No career support. You learn to train models but get no help with portfolios, resumes, or interviews.

Job market reality in 2026: LLM engineering roles outnumber traditional ML roles by a significant margin. Companies want engineers who can integrate GPT-4, Claude, and open-source LLMs into products. They need RAG systems, AI agents, and prompt engineering. Traditional ML skills like training CNNs or building recommendation systems still matter, but they are no longer the primary hiring focus. Learning ML fundamentals without LLM skills limits your job options.

Technical skills alone do not land jobs. You need a portfolio that demonstrates relevant skills, a resume that passes ATS filters, LinkedIn positioning that attracts recruiters, and interview preparation for both technical and behavioral rounds. fast.ai teaches you to build models but not to present yourself as a hireable AI engineer. Career coaching bridges this gap, often cutting job search time in half.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

It depends on your goals. If you want deep learning fundamentals for research or ML roles, fast.ai provides excellent foundations. But if your goal is landing an LLM engineering job quickly, starting with coaching focused on LLM skills may be more efficient. You can always learn ML theory later. Many successful AI engineers went straight to LLM engineering without deep ML backgrounds because the skill sets differ more than people realize.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.

Self-directed learning works for supplementing existing skills but struggles as a career transition strategy. Without external accountability, completion rates drop below 15%. Without expert guidance, you spend time on concepts that do not matter for jobs. Without feedback, you build portfolios that miss what hiring managers want. Self-directed learners often spend 12+ months preparing while coached learners get hired in 3 months.