Choose a credential for the work you want to do
Choose an AI credential around the platform you actually build on. A foundations exam can help organize learning, while an engineering credential is more useful after you have deployed and evaluated a working system.
Provider details checked: · Published by BonusMantra
Compare credentialsFollow the roadmap, choose relevant learning resources, build a project, then test your understanding with interview practice.
Explore free AI Engineer courses and a suggested learning order
For an AWS engineering role, consider the ML Engineer Associate after practical ML work. For Google Cloud roles, consider Professional ML Engineer. Use AI Practitioner only when you need foundational vocabulary or your employer values that credential.
Costs are listed in US dollars before local taxes and currency conversion; discounts, bundles and retakes can change the final price. Difficulty and “worth doing” verdicts are our editorial assessments, not provider guarantees. Recommended experience is not a formal prerequisite unless stated.
Scroll horizontally to compare all columns. Follow an exam link for its current terms.
| Provider | Exam / course name | Cost | Difficulty | Validity | Target experience | Free training options | Worth doing? |
|---|---|---|---|---|---|---|---|
| AWS | AWS Certified AI PractitionerProfessional certification | US$100 exam | Beginner / foundational | 3 years Renewal policy | Foundational AI/ML familiarity; intended for people who may use rather than build AI systems. | AWS AI foundational trainingFree foundational training is linked from the provider's preparation plan; paid extras are separate. | Optional starting point |
| AWS | AWS Certified Machine Learning Engineer – AssociateProfessional certification | US$150 standard exam; US$75 listed beta option | Intermediate | 3 years Renewal policy | About one year of AI/ML experience; check version-specific guidance. | AWS documentation and exam guideFree reading and exam guides; paid labs and full practice exams are separate. | Worth considering for AWS builders |
| Google Cloud | Professional Machine Learning EngineerProfessional certification | US$200 standard exam | Advanced | 2 years Renewal policy | Provider recommends 3+ industry years, including 1+ year designing and managing Google Cloud solutions. | Google Machine Learning Crash CourseFree ML foundations; also read the certification exam guide. Cloud labs and exam preparation subscriptions may cost extra. | Worth considering for experienced Google Cloud users |
Optional starting point
Worth doing when: Useful for learning AWS AI terminology or meeting an employer's foundational credential requirement. It can give a newcomer a finite syllabus.
Skip or delay when: Skip the fee if you already build AI systems and your target employers do not request it. Passing does not demonstrate model deployment ability.
Worth considering for AWS builders
Worth doing when: A sensible fit when your work includes deploying and operating ML on AWS. Study decisions about data, serving, monitoring and reliability alongside a real project.
Skip or delay when: Delay it if you have no practical ML work or primarily target another cloud. Check the standard/beta version before buying study material.
The provider currently lists standard and beta versions with different scope and availability. Match your study material to the exam and language you book.
Worth considering for experienced Google Cloud users
Worth doing when: Useful when Google Cloud ML architecture and operations are central to your target role. The breadth can expose gaps beyond model experimentation.
Skip or delay when: Too much platform-specific preparation for someone still learning Python or targeting an unrelated stack. Start with free ML foundations instead.
Deploy a small model or AI service, version its data, measure quality and latency, and explain one failure you fixed. That evidence makes the certification easier to defend in an interview.
Explain the problem, your decision, your test and the result. A badge can support that story; it does not establish job readiness or guarantee a salary.
