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

Some good transcription solutions: https://zapier.com/blog/best-text-dictation-software/#windowsspeech https://otter.ai/ (Haven't actually tried Otter, but it gets a LOT of good reviews.). - Source: Hacker News / about 1 month ago

Of course, there are many existing solutions like Otter.ai or Fathom in the market. But in case you want to build a tool yourself and customize the output of it, then you are on the same page as me. To develop this application, we will use Unbody to convert input video transcriptions into intelligence/generative content and Appsmith to make it easy to design and build the UI of our app without extensive front-end... - Source: dev.to / 7 months ago

This is weird but I wonder if you could use something like https://otter.ai/. Record your notes as you are going. That should give you at least text of all of your welds. You’d still have to punch it later. Seems like there’s got to be a better way to do this. Stopping every time to break your flow sounds like a huge pain in the ass. Curious what you come up with. Source: 7 months ago

Is there any app from otter.ai that you run on personal machine? How does otter.ai process 4 different audio streams? Source: 7 months ago

Job laptop -> 3.5mm aux (this turns into speaker output) -> 3.5mm mic/audio splitter (this turns into microphone input) -> 3.5mm to usb-c adapter (cause my macbook only has 1 3.5mm aux) --> now the personal macbook has a new "mic input" from the job laptop. Which you can use to pipe audio into otter.ai to transcribe audio. You have to manually name them, but they learn in subsequent meetings. Source: 7 months ago