FernandoLpz/Kubeflow_Pipelines
This repository aims to develop a step-by-step tutorial on how to build a Kubeflow Pipeline from scratch in your local machine.
This tutorial helps machine learning engineers and data scientists learn how to build and deploy reproducible machine learning workflows. It walks you through defining an ML pipeline, creating individual components for tasks like data downloading and model training (decision trees or logistic regression), and then compiling it into a deployable YAML file. The output is a structured Kubeflow Pipeline definition ready for execution.
No commits in the last 6 months.
Use this if you are a machine learning engineer looking for a step-by-step guide to set up your first Kubeflow Pipeline for MLOps on a local machine.
Not ideal if you are looking for a pre-built, ready-to-use Kubeflow Pipeline or if you are not familiar with Kubernetes and Docker concepts.
Stars
42
Forks
43
Language
Python
License
—
Category
Last pushed
Jan 26, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/mlops/FernandoLpz/Kubeflow_Pipelines"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
apache/airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
mlrun/mlrun
MLRun is an open source MLOps platform for quickly building and managing continuous ML...
clearml/clearml
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data...
argoproj-labs/hera
Hera makes Python code easy to orchestrate on Argo Workflows through native Python integrations....
argoproj/argo-workflows
Workflow Engine for Kubernetes