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For experienced ML practitioners maintaining production models and their lifecycle on Databricks.
Build production ML and Databricks operational experience.
Fits experienced engineers building and operating ML and AI solutions on Google Cloud.
Build strong programming, data processing, model deployment and Google Cloud experience.
Brings together traditional model operations and generative AI operations. Useful for engineers who own deployment, evaluation and monitoring.
Microsoft expects Python, a data-science background and experience with Azure ML, Foundry and DevOps.
Fits engineers putting models into production and maintaining reliable ML systems on AWS. Adds operational depth beyond an AI foundation credential.
AWS targets about one year using SageMaker and other AWS ML engineering services.
A platform-specific route for building generative AI solutions around Databricks data and AI services.
Practise Python, retrieval pipelines, model serving and application evaluation.
Useful for moving from notebooks into repeatable machine learning workflows on Databricks.
Practise Python, basic model training and Databricks Machine Learning.
A strong specialisation for developers building production generative AI applications on AWS, including secure retrieval, agent workflows and operational quality.
AWS targets 2+ years building production applications and one year implementing generative AI solutions.
Useful for understanding AWS AI capabilities and discussing AI solutions with technical teams. A starting point before implementation-focused exams.
A good first AI credential; learn basic AI concepts and AWS services.
The current Azure AI foundation exam for aspiring technical AI professionals. Provides a bridge into building Azure AI applications.
Learn basic Python syntax, programming techniques and Azure resources. This is more technical than a business AI credential.
Connects data engineering with agent systems by focusing on assembling and governing useful context for AI.
Build familiarity with Databricks data, retrieval and agent workflows.
A direct fit for developers creating and deploying AI apps and agents in Microsoft Foundry.
Be comfortable developing Python applications and working with Azure and generative AI.
Useful when you build generative AI applications close to data held in Snowflake.
Practise Snowflake AI features and build an end-to-end generative AI use case.
These are Global Certs IT editorial fit ratings, not official provider rankings or learner reviews. A 5/5 role match is directly aligned with the role; 4/5 adds supporting skills. Experience adjusts the score to help you distinguish a practical next step from a stretch goal.
With “Any experience”, ratings show role relevance alone. A credential supports your profile; practical skills, projects and your target employer’s technology still matter.
Official pages were checked on 2026-09-13. Bookable betas are labelled. Known retired exams are excluded from recommendations. Confirm exam versions, regional availability, eligibility and fees on the linked official page before paying.
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Deploy a model through CI/CD, track drift and latency, and demonstrate a safe rollback.