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So i have a web application that recommends movies based on the subject and question that the user puts in , it also uses NLP and ML models like Named Entity Recognition(NER) model to extract keywords and BERT model as well. I am currently just logging the login data into one of my database tables in sql . Now I also want to log the ML model and their version into the table as well .

I have a few capture points in my mind i.e when would this data be captured . One is when the user logs in and second is when the user adds the movie to the list . I thought of implementing this so that I can improve the accuracy of ML models and track performance. It will also help me to detect any kind of discrepancy if any irrelevant recommendation is made .

the plan is to just log which model was triggered when the recommendations are made and the version of it . basically i also show the keywords that are matching with the recommended movies , so probably also from which models those keywords are coming from and the version

I am just confused as to how i would go about implementing this , and wanted some inputs if anyone has worked on such a similar thing before. would be helpful. I want to implement it myself and not pay for any service

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I see two ways of doing that:

  1. Doing it with your bare hands - adding tables with information you need and programming infrastructure around and inside the model to store logs in the database. It might be enough, but it might need much work and slow down inference.
  2. Using some MLOps technology like MLflow. Implementing that first might be painful, but then you will be able to gather all the information more easily. However, it might necessitate additional infrastructure e.g. Mlflow Tracking Server
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  • $\begingroup$ I see , I was thinking of using Kafka but not sure how i will . basically capturing events in kafka and passing whatever is captured into mysql table . correct me if i am wrong $\endgroup$
    – nOhAr
    Apr 12 at 21:01

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