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Discover how to build and deploy ML models with CI/CD and Docker in this step-by-step guide designed for all aspiring AI enthusiasts! Whether you're a beginner or looking to master AI and MLOps concepts, this well-structured and easy-to-understand content will help you gain practical skills in creating an end-to-end machine learning pipeline. From setting up GitHub repositories to implementing Docker for containerization and automating deployment with CI/CD tools like AWS, this tutorial covers it all.
In this video, I walk you through integrating cutting-edge tools and techniques to efficiently manage machine learning workflows. You'll learn how to prepare data, train and evaluate machine learning models, and seamlessly deploy applications with Docker. By the end, you’ll be ready to implement professional-grade AI solutions and take your career in technology to the next level.
Join our supportive community of AI enthusiasts and dive into topics like TechnologyEducation, AiForBeginners, AiProjectImplementation, ArtificialIntelligence, and CareerSwitchTechnology. Gain confidence in your programming skills, explore practical coding strategies, and take your first step into the world of advanced machine learning and deployment.
If you found this video helpful, don’t forget to like, comment, and subscribe! Share this channel with friends and family who are passionate about technology and eager to learn. Let’s grow together as we explore the limitless possibilities of artificial intelligence.
#mlops #githubactions #cloudcomputing #devops #docker
CHAPTERS:
00:00 - Problem Statement
01:04 - Creating GitHub Repository
02:59 - Project Template File Setup
10:08 - Project Folder Structure Creation
12:19 - Writing Python Script for Folder Structure
17:18 - Project Setup Configuration
21:35 - Logger, Exception Handling, and Utilities
28:41 - ConfigBox and EnsureAnnotation Overview
31:22 - ConfigBox Implementation
32:47 - EnsureAnnotation Usage
34:59 - Project Workflow Explanation
35:22 - Jupyter Notebook Implementation
35:45 - Convert Jupyter Notebook to Python File
36:07 - Data Collection Process
37:28 - Data Preprocessing Techniques
38:36 - Model Training Overview
43:15 - Data Ingestion Methods
46:34 - Jupyter Notebook Data Ingestion Experiment
48:54 - Configuration File Setup
51:20 - Data URL Configuration
53:19 - Updating the Configuration
54:17 - Entity Creation Process
55:52 - Configuration Manager Development
58:29 - Data Ingestion Component Creation
59:27 - Pipeline Development
1:00:55 - Notebook to Script Conversion
1:07:10 - Data Validation Techniques
1:14:34 - Modular Coding Practices
1:18:42 - Data Transformation Methods
1:25:15 - Model Trainer Development
1:33:10 - Model Evaluation Techniques
1:39:55 - Prediction Pipeline Creation
1:41:00 - User Interface with HTML and CSS
1:41:30 - App.py Development
1:47:31 - Creating Dockerfile for Deployment
1:49:55 - CI/CD Deployment with GitHub Actions
1:52:35 - CI/CD Deployment Overview
1:58:06 - High-Level Architecture of Deployment
1:59:02 - Creating IAM User for AWS
2:00:53 - ECR Repository Creation
2:01:33 - EC2 Machine Setup
2:05:05 - Configuring EC2 as Self-Hosted Runner
2:07:15 - Adding Security Credentials
2:09:36 - Continuous Deployment Overview
2:12:00 - Application Testing Process
2:12:48 - Terminate EC2 Instance
2:13:02 - Delete ECR Repository
2:13:46 - Delete Elastic Beanstalk Environment
2:14:12 - Summary of Key Points
2:14:27 - Goodbye and Next Steps
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