Do AI Qualifications Really Matter? The Key Points to Focus On First
AI qualifications and certifications can serve as triggers for career changes or internal transfers, and help create a "common language" in projects. On the other hand, simply obtaining a qualification does not automatically enable you to build models. Therefore, the important thing is to choose qualifications that match your goals and connect your learning to practical work.
- Business-oriented: Understand AI terminology, limitations, and risks to reduce mishaps in planning and requirements definition
- Engineer-oriented: It becomes evidence that you can explain ML theory and implementation at a certain level
- Cloud-oriented: Easy to connect to operations skills including MLOps (training → deployment → monitoring)
Overview Map of Primary Certifications (Choose by Rough Compatibility)
If you’re undecided, start by focusing on what you want to strengthen, and you’ll find it clearer.
- Overall AI understanding (literacy): G-Kentei
- Theory to implementation of machine learning: E-Certification
- Build and operate in the cloud: AWS Machine Learning tracks (plus SageMaker and data-related services if needed)
Recommended order (the orthodox path)
Business/Non-engineers: G-Kentei → (if possible) Cloud basics → PoC experience
Engineers: G-Kentei (quick overview) → E-Certification (systematization) → AWS ML (operational differentiation)
G-Kentei (JDLA Deep Learning for GENERAL)
What kind of qualification?
The JDLA (Japan Deep Learning Association) certification tests a broad base of AI and deep learning knowledge. Rather than solving equations, it focuses on organizing concepts, history, applications, and considerations (ethics and legal aspects).
Who is it for?
- People in planning, sales, marketing, consulting, PMs, etc. who are non-engineers
- People involved in AI projects who want to break down the jargon barrier
- Engineers who want to create an overview diagram before diving into ML
Learning tips (to connect to practice)
- Avoid rote vocabulary and instead memorize what this technology is good at and what it struggles with as a pair
- Fit concepts to common internal topics (e.g., demand forecasting, churn, RAG, OCR) to understand them
- Generative AI topics change quickly, so lightly following LLM/RAG/evaluation/guardrails is advantageous
