STUDY PATH

AI infrastructure and MLOps

Every question and every answer is checked twice before you see it.

For people who run the platforms AI depends on: AI concepts, containers, GPU-based infrastructure, machine learning in production and monitoring.

See the roadmapAsk us which step to start with
4 steps6 certifications30 practice tests1,825 questions

Your roadmap

Follow the steps in order. Inside a step, choose the option that fits your platform or role. Open any card for its practice tests, study plan and what to take next.

  1. 1

    AI concepts

    What AI and machine learning are, on a major cloud.

    Choose one

  2. 2

    Cloud native base

    Kubernetes and cloud native fundamentals.

  3. 3

    AI infrastructure

    AI infrastructure and operations on NVIDIA platforms.

  4. 4

    Operate ML in production

    Build, deploy and operate machine learning, and monitor it.

Who this path is for

System administrators, DevOps and cloud engineers moving toward AI platforms.

Roles this path points toward

  • MLOps engineer
  • AI infrastructure administrator
  • Platform engineer for AI workloads
  • Site reliability engineer (SRE)

Common role titles. A certificate supports your application and does not guarantee a job.

Beyond our catalogue

  • Certified Kubernetes Administrator (CKA)

Run by their providers. Check their sites for current requirements.

Not sure where to start? Talk to us.

Tell us your background and goal and we will point you to the right first step.

Send us a messageeducation@bereadytechnology.com.au

Not affiliated with, endorsed by or sponsored by any certification vendor or exam body. Certification names, exam codes and product names are trademarks of their respective owners and are used only to identify the exam a practice product prepares for. Our questions are original and are not actual exam questions. Exams and prerequisites change, so always confirm on the provider’s official site.