50+ days of Machine Learning

 Hello :) Today is Day 139! A quick summary of today: learned how to host a mlflow server on AWS I managed to get my card working and finally finished the set up on my AWS account, so I could finish the last bit of module 2 from the MLOps zoomcamp . First, go to EC2 console And set up an instance We need to select instance type (for me it is the free one) Then we need to create a key-pair for logging in And it is done  Next we need to create an S3 bucket Next, we create a postgresql db Some configs to choose And I got a link for the db on port 5432 (which I later use for the mlflow server). We need to give access to EC2 to the database And we have the console Where we have to install some dependencies Install dependencies: pip3 install mlflow boto3 psycopg2-binary I had to create a user and add some permissions so that it can connect to S3, EC2 and RDS Then I got it running So just like when doing it locally we have the db (ofc run on aws), and the mlflow hosted as well To make sure I

 Hello :) Today is Day 138! A quick summary of today: fine-tuned microsoft's speech T5 to create a Glaswegian TTS and published it on huggingface  (using our project's dataset ) I started the day by meeting with my collaborator (as I am writing this I remembered I forgot to ask them if I can mention them by name today ...) for an hour discussing my progress last week on the Scottish phrases audio clips and how we can move forward. After the short clips, next we can move forward with transcribing podcasts, or him/friends doing voice recordings for our dataset because the 1st option might be too much manual labour.   During the call M asked me how the whole model operation happens and about embedding the text and audio, and after the meeting I decided to dig a bit and see how *once* we have a good dataset, how do we actually use it. I found this  course from huggingface that introduces working with audio data. One of the units is specifically about TTS. From there I learned about