FLOW

project based on hands-on bootcamp of MLOps course on udemy, I just follow step by step to understand how a end to end ML project organized dagshub link: https://dagshub.com/vu93.ngo/final_project

Setup

• Step 0: Prepare environment, folder structure, install lib, prepare config file

Trainning

• Step 1: Build processing step (training.src.process) -> get data from raw folder (data.raw) and return in processed folder (data.processed)
• Step 2: Train model (hyperopt need another section to learn about this lib, in this project we can skip some confused point and focus to flow first) then save to bentoml repo
• Step 3: evaluate and using dagshub to log experiment. (trainning.src.helper -> defind function to log, trainning.src.evaluate_model -> evaluate and log result)
• Step 4: consolidate in main function (trainning.src.main)

Testing

• Step 5: Test processing function with pytest, to be update: test model (training.test)

Create service

• Step 6: Load model bentoml model repo (application.src.create_service)

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• New libraries:
  • partial functions: https://www.geeksforgeeks.org/partial-functions-python/
  • pandera: https://pandera.readthedocs.io/en/stable/
• Libraries need to learn more:
  • hyperopt