[Day 188] Setting up automatically updated monitoring UI using streamlit

 Hello :)
Today is Day 188!


A quick summary of today:
  • setting up postgres properly
  • creating and hosting a monitoring interface using streamlit
  • creating a Grafana dashboard
  • setting up a batch model prediction flow in prefect


All code from previous days and today is on my repo.

First, setting up a better way to upload data to postgres

Before, everytime I ran docker the script checked if I had inserted data into postgres. Now I set up a proper sql script that inserts data once and I am set up (as long as I do not delete the built container of course)

I put all the sql and commands I used to upload the data in the postgres folder in my repo.

I first create the tables (pic is truncated)

And I also added a default column for FraudFound_P_prob just in case the user wants to get probabilities when they run a pred model. 

I uploaded 2 datasets to postgres - the above one, and one ready for the balanced random forest classifier model - it is the same as the above one but all vars are in dummies. Given that, I removed all instances in my code where I read data from local sources -> now every instance reads code from the database. 

Second, onto monitoring

I cleaned up the monitoring folder - removed LIME as I am not going to use it for this project, and I decided to use streamlit to automate the monitoring UI generation and serving. 

And it is here. I figured out how to render the html reports generated by Evidently, and also added SHAP images. Luckily for me, recently streamlit introduced pages - so the UI looks nice.

And on each:

I have 3 different reports. 

Next, I created a prefect flow that updates the artifacts (html and png files) used for the monitoring UI, and after the user uploads them to github -> the streamlit UI will be updated as well. A nice little automation piece. 

Next, I decided to use Grafana to create some kind of a visualisation dashboard there. 

I set up a connection to postgres to read data, and after some playing around, I created a basic dashboard. It is quite similar to Looker. 


Next, I started thinking how the model would be used

Rather than streaming - i.e. something that users input single data points and get an output, this type of model might be better suited for batch - use a new whole dataset and predict Fraud/Not Fraud for the whole thing. 

If I decide to (just for fun) do a web server using FastAPI, I found this nice video which shows how to host a FastAPI app for free. 

Then, I just created a prefect flow that takes in new data, the model to use, adds prediction column (and optionally preds probabilities), and finally uploads it to postgres. 

I have five prefect flows at the moment ^^



I am thinking now about adding tests, maybe creating a python documentation with sphinx since I added simple docstrings to all my code and data types. But those are tasks for tomorrow/next days.

I need to add terraform as well so that the GCP infrastructure is initialised using terraform, rather than manually (a bit more automation). 


That is all for today!

See you tomorrow :)


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