manifests VS MLflow

I have a baremetal k8s with Anthos and successfully installed ASM (Cloud Run for Anthos) on it. However, there is some conflict when trying to install Kubeflow following this repo (https://github.com/kubeflow/manifests).

That said I personally use Kubeflow hosted on a local baremetal kubernetes cluster (8 nodes, 4 gpus), but a lot of it is a bit of a bear to get installed correctly in a multi-machine environment (specifically this issue is still open and exposing the built-in dashboards outside of the cluster is a problem). Also because it's a Google product it's very clearly intended to run in the cloud with self-hosting being very much an afterthought

kind create cluster and then use the single installation command from https://github.com/kubeflow/manifests

I have deployed kubelfow v1.6 using using ArgoCD and it’s fairly simple. Every component of kubeflow has kustomze file ready kubeflow kustomize link. You just need to make argocd app for every component and then apply in that order.

For context, I run a local baremetal k8s cluster distributed over a number of machines and I've tried both [wandb](https://wandb.ai/site) and [kubeflow](https://www.kubeflow.org/), finding them to be a serious headache to manage in a local deployment. Almost none of the self-hosted builds they provide work out of the box, there's a frequent issues that have caused me significant amounts of data loss, and there are several known issues on both projects that have been open for years that make for my particular use-case difficult (e.g. [access outside of the cluster requiring a bunch of yak shaving](https://github.com/kubeflow/manifests/issues/974)).

How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

MLFlow - http://mlflow.org

MLflow:

In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.