Validates foundational skills in using generative AI at work, including AI basics, productivity tools, prompting and responsible use.
49EntryNo prerequisites
Best Artificial Intelligence Certifications in 2026
AI credentials cover three kinds of work: using AI tools in everyday jobs, building and running AI systems, and governing AI risk. Choose the kind of work first, then the platform.
Validates foundational knowledge of AI and generative AI concepts, foundation models, responsible AI and AWS AI services.
100EntryNo prerequisites
Validates foundational knowledge of AI concepts, responsible AI and building generative AI, vision and speech solutions.
99EntryNo prerequisites
Validates knowledge of AI governance foundations, relevant laws and frameworks, and governing AI development and deployment.
799IntermediateNo prerequisites
Validates knowledge of generative AI concepts, Google Cloud generative AI offerings, improving model output and leading responsible adoption.
99EntryNo prerequisites
Validates knowledge of building Azure AI solutions, including generative AI, agents, computer vision, text analysis and information extraction.
165IntermediateNo prerequisites
Validates skills in preparing data, building and tuning models, and deploying and securing machine learning workloads on AWS.
75IntermediateNo prerequisites
Validates skills in building, deploying, automating and monitoring machine learning and generative AI solutions on Google Cloud.
200AdvancedNo prerequisites
Validates knowledge of using GitHub Copilot across IDE, CLI and chat, covering prompting, data handling and policy controls.
99IntermediateNo prerequisites
Validates knowledge of designing production Claude solutions, including agent orchestration, tool integration, Claude Code, prompting and context management.
125EntryHas prerequisites
Validates knowledge of designing, integrating, evaluating and governing production Claude systems, and explaining those designs to stakeholders.
175AdvancedHas prerequisites
Validates knowledge of running AI coding agents in GitHub workflows, including tool use, state, evaluation, orchestration and guardrails.
165IntermediateNo prerequisites
Validates knowledge of AI fundamentals, business value creation, responsible AI governance and leading organizational AI transformation.
50IntermediateNo prerequisites
How to choose a certification
Which certification fits your goal?
- Everyday AI use: Google AI Essentials. Five short Google courses on using generative AI at work: prompting, everyday productivity, checking outputs and responsible use.
- AI and machine learning foundations: AWS AI Practitioner. An entry-level exam on AI, machine learning and generative AI concepts and AWS services such as Amazon Bedrock. It tests understanding rather than coding, and anyone can book it.
- AI governance: AIGP. IAPP's certification for AI governance: AI risk, laws such as the EU AI Act, frameworks and oversight of AI development and use. It suits privacy, legal, risk and product professionals.
- Machine learning implementation on AWS: ML Engineer – Associate. Covers preparing data and building, deploying and running ML models on AWS. AWS recommends a year of hands-on experience with its ML services.
- Machine learning on Google Cloud: Professional ML Engineer. Covers building, deploying and monitoring ML and generative AI solutions on Google Cloud. Google recommends three years of industry experience.
What to know before choosing
- Separate AI use from AI engineering. Using AI tools well and building production AI systems are different skills. Pick the credential whose tasks match your work.
- Study for the current exam version. AI exams are revised often: AWS's ML Engineer exam is moving to MLA-C02, and Google revised its ML Engineer exam guide in June 2026. Use study material written for the current version.
- Apply it to a real project. For technical credentials, build and evaluate a small solution, including data handling and monitoring. For governance, apply the material to a real AI use case and its risks.