I am running mlflow server with local host inside a vm and using the same as tracking uri, but when I do start_run() I get an error of 400 or 403. How do I resolve this.
Great video, thanks. I did have one question; is there a way to also version control the data that the model was trained on? It is super useful to be able to track several versions of the model in production, but in my usecase I also may want to also track the dataframe that each version of the production model was trained on. Can MLFlow do this or do you know any other tools that can easily integrate with MLFlow for this?
I am running mlflow server with local host inside a vm and using the same as tracking uri, but when I do start_run() I get an error of 400 or 403. How do I resolve this.
Great video, thanks. I did have one question; is there a way to also version control the data that the model was trained on? It is super useful to be able to track several versions of the model in production, but in my usecase I also may want to also track the dataframe that each version of the production model was trained on. Can MLFlow do this or do you know any other tools that can easily integrate with MLFlow for this?
DVC is for data version control
Very helpfull bud, thanks a lot.
Hi can you make a tutorial on how to deployu to prod already trained models?
不错不错