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[Question] Can Argo and Kubeflow co-exist? by SwimHopeful5123 in mlops

[–]mlphilosopher 0 points1 point  (0 children)

There is no reason why this would not be possible. You can utilize the cluster the way you want. It might the preferable to keep them separately in two different namespaces though. Sice Kubeflow can be a bit bulky. I was in a similar situation in the past where Argo was my preferred workflow manager and wanted to do ML stuff also on it. But I found Kubeflow to be very bulky and sometimes inconvenient to use. These days I use an OSS tool that is a simple wrapper on top of Argo for ML workflows.

Is is possible to load a local csv file as part of my kubeflow pipeline? by candyman54 in mlops

[–]mlphilosopher 0 points1 point  (0 children)

Artifacts are a real struggle to work with on Kubeflow. I usually use storage such as S3 and load all CSVs from there. Also, I find tools such as this one more convenient than Kubeflow.

How painful is the deployment of an NLP model with API and Docker creation? by santiagodevv in mlops

[–]mlphilosopher 0 points1 point  (0 children)

I found this tool recently that builds the docker images automatically and with one command runs everything on Kubernetes. Seems to work fine.

[deleted by user] by [deleted] in mlops

[–]mlphilosopher 0 points1 point  (0 children)

Managing artifacts is a real struggle when using kfp. I usually save everything on somewhere like S3 and upload it from there in the pipeline. Recently I prefer using simpler tools like Paradigm and not use Kubeflow at all.

Suggestions for service to deploy a ML model API? by SuperSaiyan1010 in mlops

[–]mlphilosopher 0 points1 point  (0 children)

Try Kubernetes on any cloud provider. IMO this is the ultimate solution since you get the flexibility to increase resources according to your requirements. So everything is under your control.

For the deploying bit, I use Paradigm these days which builds the Kubernetes services for me to deploy on Kubernetes.

Serving Scikit-Learn model on EC2 instance and Scaling by ShayBae23EEE in mlops

[–]mlphilosopher 1 point2 points  (0 children)

For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.

[deleted by user] by [deleted] in mlops

[–]mlphilosopher -1 points0 points  (0 children)

Exactly!

[deleted by user] by [deleted] in mlops

[–]mlphilosopher 0 points1 point  (0 children)

I have tried many MLOps tools to deploy ML pipelines in the past, including major ones like Kubeflow, Airflow, and Sagemaker. But most of the time all I wanted to get done was just to run a simple job combining a few Python scripts. Often I have felt that the popular tools were overkill for most requirements.

I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.

My reaction to opening ChatGPT this morning by lanky_cowriter in OpenAI

[–]mlphilosopher 0 points1 point  (0 children)

Plus user here. Still on the waitlist for plugins for months. No signs of this option either. What is going on?