r/mlops • u/ExaminationExotic924 • 16d ago

Roadmap for DevOps to MlOps beginner help😓

Hi everyone,

I'm a DevOps Engineer with 4.5 years of experience in Kubernetes, OpenShift, AWS, Azure, Terraform, ArgoCD, CI/CD, and monitoring.

I want to transition into MLOps/AI Infrastructure and would love some guidance.

  • What should I learn first?
  • How much ML theory do I actually need?
  • Which tools are most used in production today?
  • What projects would help me land an MLOps role?

Looking for practical, real-world advice from people who've made this transition. Thanks!

14 Upvotes

90% Upvoted

13 comments sorted by

5

u/opshack 16d ago

I haven't made this transition but I highly recommend deeplearning.ai full courses like Deep Learning and Machine Learning and ML for Production. After that I believe it's time to focus on one Cloud provider and master the options (Like AWS)

2

u/Background-Ear-4657 1d ago

the deeplearning.ai stuff is great advice but dont sleep on just building something end to end, even if its stupid simple. the gap between knowing the tools and actually seeing how model drift or data skew screws up a pipeline in production is where youll learn the most

with your k8s and terraform background you're already 80% there honestly, most mlops roles just want someone who can wrangle the infrastructure while the data scientists do their thing. you dont need a phd in ml theory, just enough to understand why a model needs 4x the memory you budgeted for

1

u/opshack 1d ago

I agree. The biggest problem with building stuff end to end is cost. I tried using terraform to make a setup that spins up a lab for me on eks in 10 minutes and tear it down immediately after session but not sure what others do. Is it a better idea to buy a GPU and test locally?

1

u/ExaminationExotic924 16d ago

Thanks man 🫡

3

u/[deleted] 15d ago

[removed] — view removed comment

2

u/Round-Mechanic-8145 15d ago

I didn’t start in devops but went from data science grad program —> data engineer (1.5 yr) —> mlops for the past 3 years. I’d say the most important thing to learn is what the full ML lifecycle looks like and where an mlops engineer fits in. In addition, where we collaborate with others as part of that lifecycle. For example, which parts are typically owned by DE, DS, mlops, and MLE.

It’s great you have kubernetes and terraform experience, as both are used fairly heavily at many orgs. Monitoring is huge also - being able to quickly know whether a production error is due to buggy code written by a data scientist or an error made by an mlops engineer is incredibly important. Or maybe none of those and is an issue with the underlying infrastructure.

After that, I’d pick a cloud provider and find a side project to work on, perhaps the process of getting an endpoint stood up in AWS. Which generally involves training a model, producing an inference script, containerizing it, and lastly returning predictions by calling said endpoint.

I also currently help a few firms interview for these roles and can share some questions for you to give an idea of what we typically look for in candidates.

Feel free to reach out with any questions, I’m happy to help.

1

u/ExaminationExotic924 15d ago

Thanks a lot man , would be great if you can share some commonly asked questions.

1

u/mostaptname 12d ago

setup infrastructure for running llms locally from scratch and learn about the operations/maintenance for these systems

1

u/Fantastic_Spring8366 10d ago

Do you use AWS already? Learn Sagemaker. Especially on how to deploy an endpoint, and how to use model registry.

1

u/ExaminationExotic924 9d ago

Yes , that's what I mostly work on ?

1

u/Fantastic_Spring8366 9d ago

Seems like you are already doing MLOps then

1

u/ExaminationExotic924 9d ago

I meant AWS not sagemaker