Raw journaling while building a project is the best things to do, daily journaling writing
the problem: i'm stuck understanding docker,airflow,mlflow internal working
i'm not getting what is Volumes,Ports and how they in docker with MLflow, i want to understand it by creating a new toy project which would be using mlflow and it will me understand how experiments, tracking and data works in mlflow
Successfully Ran the Airflow DAG.
No Errors. each steps executed smoothly.
As scheduled it's running the project in every 15 min.
It ran the project, gave the output - Recall : 0.96.
Airflow schedules the projects and runs in every 15min/hour/day.
No need to run the project manually, it automates and orchestrates the project, each step is executed after each one.
MLFlow tracks the performance, params, evaluation metrics of project store. Logged in
MLFlow tracking the project assigned airflow in it.
Errors i got and how i solved,
the very first error i got
Segmentation fault error:
the error was coming cause the file path was not correct gave it the right path it ran.
Then it gave the error again.
i changed the n_jobs=-1 to n_jobs=4, using less cores in Airflow.
Simply i read docs understood the topics and concepts and executed it used very simple code
made it much simpler just to orchestrate and schedule the project locally.
All the files and codes are running locally
i'm building an end to end mlops project- Telecom Customer Churn.
Predicting customer churned or not.
the stack i'm using in it
i'm a beginner in project building
these are the stack i'm using i didn't know which to use first and which to second, so i did which seemed easier, starting with builiding the model then streamlit,fastapi,mlflow,airflow and docker .
I don't how they are made production ready. I'm updating the project Progess on X(twitter)
here : https://x.com/anandrishv
github : https://github.com/rishv1912/Customer-Churn-MLOps
i'm done 50% just have to do each step one by one and i'm done.
if you have any advice or anything can you tell me how to do it. i'm a core ML(supervised learning) so yeah.
Thanks everyone, if you have read till here
Coding and experimenting with oops, tell something that can enhance my learning in practice and can challenge me to think .
things i have to cover.
class
Objects
constructor
__str__
instance,class,static methods
decorator
Encapsulation
private
protected
getter
setter
property
Inheritance
single & multiple inheritance
Polymorphism
overloading
overriding
Abstraction
ABC
abstractmethod
am i missing anything ?