Скачать или смотреть End to end Deep Learning Project Implementation using MLOps Tool MLflow & DVC with CICD Deployment 🚀

Code: https://github.com/entbappy/End-to-En...

"🚀 Dive into the world of MLOps with my comprehensive tutorial on implementing an end-to-end deep learning project! In this video, we explore the power of MLflow & DVC to streamline the machine learning lifecycle, from data versioning to model deployment.

🔍 Discover how to effectively manage and version your pipeline using DVC (Data Version Control), ensuring reproducibility and easy collaboration among team members. Learn the ropes of MLflow for tracking experiments, packaging code, and sharing models seamlessly.

🧠 Master the art of continuous integration and deployment (CI/CD) specific to machine learning models, empowering you to automate testing, training, and deployment pipelines effortlessly.

🔧 Whether you're a beginner or an experienced data scientist, this tutorial provides valuable insights and hands-on guidance for implementing MLOps practices into your deep learning projects.


▬▬▬▬▬ Contents of this video ▬▬▬▬▬

00:00:00 - Introduction
00:10:50 - Project Demo
00:15:34 - MLflow Demo
01:03:09 - MLflow with Dagshub
01:20:04 - MLflow with AWS
01:44:34 - Github Repository Setup
01:47:08 - Project Template Creation
01:59:52 - Requirements Installation & Project Setup
02:06:31 - Logging, Exception & Utils Modules
02:26:58 - Project Workflows
02:30:02 - Data Ingestion Component
03:08:16 - Prepare Base Model Component
03:28:35 - Model Trainer Component
03:41:37 - Model Evaluation Component & MLflow Integration
04:23:09 - DVC Pipeline
04:48:03 - Prediction Pipeline & User App
04:57:09 - Dockerization & AWS CICD Deployment


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