Hey, the founder of dstack here. If I put it shortly, dstack allows you to define ML workflows as code and run them either locally or remotely (e.g. in a configured cloud). ML workflows here mean anything that you may want to do when you're developing a model - prepping data, training or finetuning a model, etc. The value - basically it automates running your workflows, without being dependant on any particular vendor. At the same time, you don't have to rewrite your Python scripts to use a particular API (because dstack is using YAML).

We aim to build the most easy tool to run ML workflows - without making you use the UI of any vendor, or hustling with Kubernetes, custom Docker images, etc.

MLFlow doesn't do what dstack does (automatic infrastructure provisioning) - unless you use Databricks.