End To End Deep Learning Project | Euron

Sign up with Euron today : https://euron.one/sign-up?ref=940C6863 One Student One Subscription Euron Plus - https://euron.one/personal-plan/aa2904bd-b41c-407a-b912-9dd8c75d5637?ref=940C6863 Project Link : https://euron.one/course/chicken-diseases-classification Call or WhatsApp us at: +919110665931 / +919019065931 🚀 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 Instagram: https://www.instagram.com/euron_official/?igsh=Z3A3cWgzdjEzaGl4&utm_source=qr WhatsApp :https://whatsapp.com/channel/0029VaeeJwq9RZAfPW9P2l07 LinkedIn: https://www.linkedin.com/company/euronone/?viewAsMember=true Facebook: https://www.facebook.com/people/EURON/61566117690191/ Twitter :https://x.com/i/flow/login?redirect_after_login=%2Feuron712