/basic-ml-pipeline

Based of these blog posts. https://towardsdatascience.com/a-simple-mlops-pipeline-on-your-local-machine-db9326addf31

Primary LanguageJupyter Notebook

Basic Machine Learning Lifecycle Pipeline

Purpose

Build a basic mL pipeline using open source technology today

Tech used in this pipeline:

  • Python
  • Docker/Kubernetes
  • Mlflow
  • Minio
  • Seldon Core
  • Github
  • Jenkins
  • ArgoCD

Setup the experiments

  1. Go into experiments
  2. Run docker-compose up -d to start up the underlying services

Training the Experiment with mlFlow

  1. Run docker exec -it mlflow_server python train.py to kick off the training and experiments

Build API Endpoint for model prediction

Test with microservice: seldon-core-microservice MyModel REST --service-type MODEL

Credits

Build Docker File with Python Wrapper Class

A Simple MLOps Pipeline on Your Local Machine