End TO End Deep Learning Project | Euron
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Unleash your potential with this comprehensive guide to mastering AI deployment, covering everything from data preparation to accurate predictions. Whether you're a beginner or looking to refine your skills, this tutorial walks you through creating a deep learning pipeline step-by-step with practical examples and cutting-edge tools.
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🌟 What you'll learn:
Build an end-to-end AI project using Python and TensorFlow.
Understand CNN architecture for tasks like email classification and chicken disease detection.
Set up GitHub repositories, manage MLOps tools, and utilize cloud platforms like AWS.
Explore advanced deployment strategies with CICD and Docker for real-world applications.
Perfect for beginners, this free course is packed with actionable insights to kickstart your programming journey and elevate your AI expertise. Hit play and code along to gain hands-on experience in creating impactful solutions.
💡 Why watch this video?
Structured, easy-to-follow tutorial with real-world datasets.
Practical demonstrations of deep learning applications.
Step-by-step guidance on deploying AI models seamlessly.
Don't miss out on this opportunity to learn AI deployment like a pro! Like, Subscribe, and hit the notification bell to stay updated with Euron . Let's embark on this exciting coding adventure together!
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CHAPTERS:
00:00 - Introduction
01:49 - Agenda Overview
03:51 - Project Prerequisites
04:46 - Problem Statement Analysis
06:36 - Project Overview and Goals
06:50 - Creating GitHub Repository Setup
08:20 - Project Template Creation
11:42 - Project Setup Process
15:45 - Custom Logger Implementation
17:57 - Utility Function Development
19:53 - Importance of ConfigBox
23:55 - Significance of EnsureAnnotation
26:11 - Project Workflow Explanation
27:22 - Data Ingestion Process
27:57 - Data Ingestion File Creation
29:38 - Understanding Data Structure
32:00 - Uploading Data to GitHub
34:00 - Config File Path Update
39:50 - Converting Notebook to Python Code
46:14 - Prepare Base Model Steps
53:10 - Updating params.yml Configuration
55:33 - ConfigurationManager Creation
58:15 - PrepareBaseModel Component Development
59:10 - Notebook to Pipeline Code Conversion
1:02:59 - Model Training Process
1:11:16 - Model Evaluation Techniques
1:17:44 - Prediction Pipeline Development
1:19:35 - User Application Overview
1:23:04 - Creating Web Application
1:27:25 - Dockerfile Setup
1:28:15 - GitHub Workflow Configuration
1:29:55 - CI/CD Workflow Setup
1:31:24 - CI/CD Deployment Process
1:37:10 - High Level Architecture Overview
1:39:45 - AWS Login & IAM User Creation
1:39:55 - ECR Repository Creation
1:40:37 - EC2 Instance Setup
1:42:32 - EC2 Instance Configuration
1:44:08 - Configuring EC2 as Self-Hosted Runner
1:46:17 - Adding Secrets to GitHub
1:48:39 - Starting CI/CD Process
1:50:05 - Running Application Overview
1:51:42 - Terminating Resources
1:53:15 - Outro and Summary
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