It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: almost 2 years ago

Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 2 years ago

There's dozens of tools out there for this these days. I'd recommend sonix.ai they give you 30 minutes free. Source: about 1 year ago

Do you have a budget? If so, there's this tool I've worked with called Sonix that generates transcripts of what you feed into it. It's not super accurate, but it's good enough. One of the features is that you can "highlight" chunks of text, and have it spit out an XML that will have a sequence containing only the highlighted text. Source: over 1 year ago

Sonix was the one I used because it had 30 free minutes and the video was only 10-11 minutes long. It seems to have done a really decent job, but not sure if that's because the source audio is pretty clear. Source: over 1 year ago

Sonix.ai does many languages and is quite good. Source: over 1 year ago

I am struggling with this as well, but one good tool for me has been sonix.ai, which can transcribe pretty well (posted a little while ago about it). Source: about 2 years ago