๐ GitHub Repo: https://github.com/iQuantC/Kubeflow-pipeline.git
Description:
In this video, we dive deep into the world of Kubeflow Pipelines and show you how to deploy a complete Machine Learning workflow locally using Docker and Minikube. Whether you're a data scientist, ML engineer, or just curious about MLOps, this tutorial will guide you step-by-step through the process of:
1. Loading Data: Learn how to efficiently load your dataset into the pipeline.
2. Preprocessing Data: Discover best practices for cleaning and transforming your data.
3. Training a Machine Learning Model: Train your model using Kubeflow's powerful orchestration capabilities.
4. Evaluating the Model: Evaluate your model's performance and ensure it meets your expectations.
Tools & Technologies Used:
1. Kubeflow Pipelines: For orchestrating the ML workflow.
2. Docker: For containerizing your pipeline components.
3. Minikube: For running a local Kubernetes cluster.
By the end of this video, you'll have a fully functional Kubeflow Pipeline running on your local machine, ready to handle your ML projects with ease.
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#Kubeflow #MachineLearning #MLOps #Docker #Minikube #DataScience #MLPipeline #AI #DataEngineering #Kubernetes #DevOps
Timestamps:
0:00 - Introduction
1:08 - Code Overview
11:45 - Setting Up Docker Minikube & Kubectl
17:44 - Setting Up Python venv
21:25 - Setting Up Kubeflow environment
29:54 - Build and Deploy Kubeflow Pipeline
35:15 - Create & Run Pipeline in Kubeflow UI
42:40 - Clean Up
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Disclaimer: This video is for educational purposes only. The tools and technologies demonstrated are subject to change, and viewers are encouraged to refer to the official documentation for the most up-to-date information.
Thank you for watching! ๐