Amber enrol buttons are DataCamp and Udemy affiliate links; we earn a commission if you enrol through them. How we're funded.
The three big clouds — AWS, Microsoft Azure, and Google Cloud — all offer AI certifications, and they're all valuable. But they're not interchangeable. The right one depends on the jobs you want and the tools your target employers use. Here's a clear, honest comparison, plus the best course to prepare for each.
Quick answer
Pick the cloud your target employers already run on — that one rule settles it for most people. AWS has the most job openings, Azure matters in Microsoft shops, and Google Cloud carries the strongest machine-learning-engineering signal. All three are valuable and none is interchangeable: a Google Cloud credential is worth little in an AWS shop, and the reverse is equally true.
AI-102 is retired. The replacement is Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103).
Exam AI-900: Microsoft Azure AI Fundamentals is retired. The replacement is Exam AI-901: Microsoft Azure AI Fundamentals, which earns the same Azure AI Fundamentals certification but expects Python and familiarity with REST APIs and SDKs.
Where we would start on DataCamp or Udemy
We choose these picks only among our affiliate partners’ courses (365 Data Science, DataCamp and Udemy). Our full ranking also includes courses that earn us nothing.
The vendor-neutral answer to this page's question: if you do not yet know which cloud you will work in, learn the layer that is the same on all three.
Why this course, and its limitations
The current overall score reflects our emphasis on an applied syllabus: APIs, embeddings, vector databases, LangChain and LLMOps. The compact format can suit someone already comfortable with Python. Its limits are theoretical depth and credential scope: track completion does not award the separate DataCamp certification. We have no hiring-outcome or completion-rate data for this track.
Learning: 4.8/5. Credential: 3.0/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
If AWS is where you have landed, this is the hands-on counterpart to the exam guide: SageMaker, Bedrock and MLOps rather than slides.
Why this course, and its limitations
Preparation for the AWS Machine Learning Engineer Associate exam. We value the specific exam-preparation goal. The AWS credential is awarded through the separate exam, not by completing this Udemy course.
Learning: 4.4/5. Credential: 3.5/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
Side-by-side comparison
AWS has the highest market share and job demand, Google Cloud the highest technical rigor and the strongest salary signal, and Azure the firmest hold on enterprise Microsoft shops. Here are all three on every factor we scored.
| Factor | AWS | Azure (Microsoft) | Google Cloud |
|---|---|---|---|
| Market share / job demand | Highest | Strong (enterprise) | Growing |
| Best entry credential | AI Practitioner | AI-901 | Generative AI Leader |
| Flagship ML credential | ML Engineer – Associate | AI Apps and Agents Developer (AI-103) | Professional ML Engineer |
| Technical rigor | High | High | Highest |
| Salary signal | Strong | Strong | Strongest |
| Best for | Most cloud jobs | Microsoft shops | Serious ML engineering |
AWS AI certifications
AWS dominates cloud market share, so AWS AI skills appear in the largest number of job postings — making it the safest default if you're unsure. The entry-level AWS Certified AI Practitioner is a great starting signal. Beyond it, AWS retired the Machine Learning – Specialty exam on 31 March 2026, and the Machine Learning Engineer – Associate exam has an updated version, MLA-C02 (AWS set 28 September 2026 as the last English sitting of MLA-C01), so check AWS's official certification page for the current options before planning a path. Our AWS AI certifications guide sets out all three AWS AI exams, what changed, and which to take first.
Best way to prepare: the "Introduction to AI and Machine Learning" course on Coursera (published by LearnKartS, not AWS itself) maps directly to the exam — no coding required.
Read our AWS AI Practitioner guide →
Start the AWS Prep Course on Coursera →Azure (Microsoft) AI certifications
If you work in (or want to work in) a Microsoft-centric enterprise, Azure is the obvious pick. The path runs from AI-901 (Azure AI Fundamentals, which replaced AI-900 on 30 June 2026 and expects basic Python) to AI-103 (AI Apps and Agents Developer, which replaced the retired AI-102). Microsoft tooling is everywhere in large companies, so these certs carry real weight there.
Best way to prepare: the Microsoft AI & ML Engineering Professional Certificate on Coursera teaches Azure AI/ML end to end.
Start the Microsoft Prep on Coursera →Google Cloud AI certifications
The Google Cloud Professional Machine Learning Engineer is the most technically rigorous of the three and the one most associated with a salary premium. It's aimed at people doing real production ML — not beginners — and signals serious ML-engineering ability.
Best way to prepare: Google Cloud's official "Preparing for Google Cloud ML Engineer" professional certificate on Coursera.
Start the Google Cloud Prep on Coursera →Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with. It suggests only our affiliate partners’ courses, and says so before it suggests anything.
Try the AI Certification Picker →How to choose (the simple rule)
Pick the cloud your target employers already run on: AWS for the most openings, Azure for Microsoft shops, Google Cloud for the strongest ML-engineering signal.
- Not sure / want the most jobs? → AWS.
- Your company/target employers use Microsoft? → Azure.
- You want the most respected ML-engineering credential and top salary signal? → Google Cloud.
- Not cloud-committed yet? Build vendor-neutral ML foundations first with the Machine Learning Specialization, then specialize in a cloud.
Ready to start?
Included in a DataCamp subscription rather than bought outright. DataCamp's pricing page shows the plans and the price for your country, and one subscription covers the rest of its catalogue too.
Frequently asked questions
Which cloud AI certification is best: AWS, Azure, or Google Cloud?
It depends on the job market you are aiming at, not on which platform is technically better. AWS holds the highest cloud market share, so AWS AI skills appear in the largest number of postings — the safest default when you have no specific employer in mind, starting with the AWS Certified AI Practitioner. Azure is the obvious choice inside Microsoft-centric enterprises, where the tooling is already everywhere; the path starts at Azure AI Fundamentals (now exam AI-901, which expects basic Python) and steps up to AI-103, which replaced the retired AI-102. Google Cloud's Professional ML Engineer is the most technically rigorous of the three and carries the strongest salary signal, but it assumes the most going in.
The rule that settles it: pick the cloud your target employers already run. A certificate only pays off where the platform is actually deployed, and no amount of rigour compensates for certifying on a stack nobody in your market uses.
Which cloud AI certification pays the most?
The Google Cloud Professional Machine Learning Engineer is the credential most associated with a salary premium, because it is aimed at people already doing production machine learning rather than at newcomers — the pay reflects who takes it as much as what it teaches. AWS and Azure machine-learning roles pay well too, and AWS's larger share of postings means more chances of finding one.
Read any of these figures carefully. Role and experience move salary far more than the brand on the certificate does, and the highest-paying credential is also the hardest to reach from a standing start. If you are early, the sequence that works is the entry credential on your chosen cloud first — AI Practitioner on AWS, Azure AI Fundamentals (now AI-901) on Azure — then the advanced one once you are doing the work it describes. Certifying ahead of the job rarely converts into pay on its own.
Can I learn more than one cloud?
Yes, but master one first. Cloud AI services differ in naming and packaging far more than in underlying concepts, so the second platform is much faster to learn than the first — and shallow familiarity with three clouds reads worse in an interview than genuine fluency in one. Pick the cloud your target employers use, get proficient enough to build and deploy something on it, and add a second later if a role actually calls for it.
If you are not committed to any cloud yet, there is a better first step than choosing one. Build vendor-neutral machine-learning foundations with the Machine Learning Specialization, then specialise. Those concepts transfer to every platform, whereas a cloud certification earned before you understand the underlying models teaches you a console rather than a subject.