[Day 184] Mlflow experiment tracking and trying out metaflow

 Hello :)
Today is Day 184!


A quick summary of today:
  • got various experiments ran and saved in my GCP mlflow server
  • set up and tried out metaflow for MLOps orchestration


Today I had to re-create my GCP VM because of a python version issue. So after starting a VM with python 3.10 I could use mlflow's most current logging functionality. 

I ran a few experiements and the currently saved are:

I will select a model solely based on recall - being able to detect the most fraud cases out of all fraud cases. Based on that, so far (after finding optimal params) the Balanced RF Classifier is the best -> recall being 0.92. 

The most recent code and files are in my repo. 

Today I did some refactoring to make things look nicer -> added a utils folder with some helper functions that I used a lot throughout notebooks. The current are log_model_performance and get_best_params which log a model run to mlflow, and then load the params of a model from mlflow. 

Another thing - I asked on reddit's MLOps page what is a good tool to tryout because so far I know only Mage as an orchestrator, and what is a good, popularly used tool, for beginners and for an MLOps project. Metaflow got the most votes, but also flyte. I decided to first check out metaflow as it seems it is widely used in top companies like Netflix and LinkedIN.

metaflow has an UI which can be set up in the cloud (prefered method) or locally (which I did following this article).

I found a simple metaflow workflow, and modified it as follows:

The code for it is on my repo as well, under testing_metaflow.py

I am running the metaflow UI locally and the below is what I get in the UI (given a success run)

Going into one of the 'green' runs:

However, I am not seeing anything else. I am not sure if it is because I need to initialise it in my code, or maybe because I am running the UI locally. Nevertheless, it seems nice (I guess) but I need to figure out logging more info and maybe artifacts. 

Tomorrow ~ I will try to explore metaflow a bit more. Hopefully I can figure out how to see more things in the UI.



That is all for today!

See you tomorrow :)

Popular posts from this blog

[Day 198] Transactions Data Streaming Pipeline Porject [v1 completed]

Image
 Hello :) Today is Day 198! A quick summary of today: data streaming pipeline project [v1 done] Here is a link to the project's repo. Well ... I did not know I can do it in a day (~14 hours) after yesterday's issues but here we are. Turns out in order to insert the full (~70 variables with nested/list structure), I need the proper pyspark schema. And yesterday I did not have that and that is why when I was reading data in the kafka producer I was getting NULL in the columns - my schema was wrong. Well today I not only fixed the schema for the 4 variables I had yesterday, but included *all* the variables that come from the Stripe API ~ 70 (for completeness).  When I run docker-compose, the data streams and is input into the postgres db (and is still running). Unfortunately, the free Stripe API for creating realistic transactions has a limit of 25, so every 3 seconds, 25 new transactions are sent to the db. It has been running half the day (since I got that set up) and as I am wr
Read more

[미리 공부] 기초 통계 복습 (Day 1는 1월2일)

Image
모험을 시작하기 전에 기초 지식을 복습하고자 했다. 오늘 SPSS 대안 프로그램을 찾아보려고 해서 JASP에 대해 알게 되었다. 유용한 프로그램인 것 같아서 선회귀와 기술통계를 내려고 했는데 재미있었다. 그런데 JASP에 대해 더 알기 전에 '기초 통계 지식을 좀 복습을 하고자 하면 좋을 것 같아'란 생각을 들었다. 다행히, Coursera에서 Stanford University의 Guenther Walther 교수님께서 진행된  Introduction to Statistics  무료 강좌가 있다.  좀 부족한 부분은 다양한 검정통계 하는 거고 (F test, t-test, chi-square 등) 이제 JASP 아니면 다른 통계 프로그램 사용하게 되어도 이런 부분을 좀 더 자세히 집중하여 공부하면 된다.  특히 homoscedasticity 및 heteroscedasticity 개념을 기억에 남았다. Homoscedasticity (선): Definition: In a homoscedastic dataset, the variance of the errors (residuals) is constant across all levels of the independent variable(s). In simpler terms, the spread of the residuals is the same throughout the range of predictor values. Heteroscedasticity (악): Definition: Heteroscedasticity occurs when the variance of the errors is not constant across all levels of the independent variable(s). In other words, the spread of residuals changes as the values of the independent variable(s) change. (Udemy 사진) 강의
Read more

[Day 61] Stanford CS224N (NLP with DL): Machine translation, seq2seq + a side CDCGAN mini project

Image
 Hello :) Today is Day 61! A quick summary of today: Covered Lecture 7 : machine translation, seq2seq, attention from Stanford CS224N Tried to make a conditional DCGAN to generate MNIST numbers ( colab ) ( kaggle ) I will first cover the GAN story (then will share my notes from the lecture) So... while watching and taking notes today, I started thinking, what if I can use my notes as data to a model and afterwards, when I want, I can give it raw string text and it will output text in the format of my notes (with my handwriting). Well I started looking around and actually the first model architecture that came to my mind was the GAN (specifically conditional GAN) - I remembered there was a GAN architecture that alongside the pictures, we can give it the labels, and then on-demand generate. In retrospect, there are of course others, but I decided to go with GAN.  For maybe 2 hours I busted my head trying to make a simple model with the EMNIST dataset (english characters), and I kept gett
Read more