๐ Kubeflow Pipeline for End-to-End Machine Learning Workflow | Tutorial | For Beginners
About this lesson
Support Us: https://buymeacoffee.com/iquantconsult 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. ๐ If you found this video helpful, please give it a thumbs up and subscribe for more tutorials on Machine Learning, MLOps, and Data Science! ๐ข Let us know in the comments if you have any questions or need further clarification on any of the steps. We're here to help! #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 Subscribe to our channel for more in-depth tutorials and stay updated with the latest in Machine Learning and Data Science! Disclaimer: This video is for educational purpose
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