End To End Deep Learning Project | Euron
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🚀 Ready to master Deep Learning Deployment? This comprehensive guide takes you through every step of building and deploying end-to-end deep learning projects with ease! Whether you're a beginner or looking to refine your skills, this tutorial has got you covered.
🔑 What you'll learn:
Create a complete Deep Learning pipeline using CNN architecture for image classification.
Implement advanced projects with MLOps tools for seamless deployment.
Learn to work with TensorFlow, Python, and cloud platforms like AWS.
Solve real-world problems like chicken disease classification using AI.
🎯 Perfect for:
Beginners aiming to kickstart their deep learning journey.
Professionals wanting to explore MLOps and cloud deployment.
Anyone passionate about building practical deep learning solutions.
💡 Why watch this video?
Hands-on project walkthroughs with step-by-step explanations.
Learn to build and deploy scalable applications using CI/CD pipelines.
Clear guidance on setting up GitHub repositories, logging, exception handling, and utility management.
Bonus: Insights on object detection, image segmentation, and NLP projects!
🌟 Don’t miss out! Hit play and code along to transform your skills. Like, Subscribe, and hit the notification bell to stay updated on all things AI and Deep Learning.
📌 Your deep learning deployment journey starts now. Let’s build something amazing together! 🔥
#mlops #machinelearningprojects #dataversioning #tensorflow #datascience
#cnn #mlops #machinelearningprojects #mlopsplatform #deeplearningdeployment
CHAPTERS:
00:00 - Introduction
01:49 - Agenda Overview
03:52 - Prerequisites for Project
04:47 - Defining the Problem Statement
06:46 - Creating a GitHub Repository
08:38 - Setting Up Project Template
11:42 - Project Requirements Setup
15:48 - Creating Logging and Utility Components
19:56 - Reading YAML Configuration Files
23:55 - Importance of @Ensure Annotation
26:12 - Understanding Project Workflow
27:19 - Developing Data Ingestion Component
29:35 - Data Collection Techniques
34:00 - Configuration and Parameters
39:56 - Model Encoding Process
46:10 - Preparing the Base Model
53:10 - Updating params.yml File
55:39 - Creating Configuration Manager
58:17 - Preparing Base Model Component
59:10 - Converting Notebook to Python Code
1:02:55 - Model Training Process
1:11:15 - Evaluating Model Performance
1:17:54 - Building the Prediction Pipeline
1:19:36 - User Application Development
1:23:25 - Cloud Deployment of Project
1:27:38 - Creating Dockerfile for Deployment
1:28:13 - Setting Up GitHub Action File
1:28:59 - Initiating Deployment Process
1:31:25 - Understanding CICD Deployment
1:37:10 - High-Level Architecture of Deployment
1:38:05 - Creating IAM User for Access
1:39:57 - Setting Up ECR Repository
1:40:38 - Creating EC2 Instance
1:42:32 - Configuring EC2 Instance
1:44:08 - Setting Up EC2 as Self-Hosted Runner
1:46:17 - Adding Security Credentials
1:48:39 - Continuous Integration Process
1:49:54 - Continuous Deployment Strategies
1:51:02 - Training the Machine Learning Model
1:53:14 - Outro and Summary
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