Скачать или смотреть Deploying Machine Learning Models to The Cloud

Welcome to Yacooti’s comprehensive Machine Learning Deployment Guide! This course will walk you through deploying a machine learning model from training to production, ensuring that your model is accessible to users worldwide. Whether you’re a beginner or an experienced developer, this guide will help you set up your machine learning project for real-world use.

Course Overview
This course covers step-by-step deployment of a machine learning model, including:

1. Preparing Your Environment
Prerequisites: Installing Docker, setting up a Docker Hub account, and budgeting for a domain and cloud hosting.
Setting up your project by cloning the GitHub repository and training your model.

2. Building and Pushing Docker Images
Writing a `Dockerfile` to containerize your model.
Creating a `docker-compose.yml` file for efficient deployment.
Building and pushing images to Docker Hub.

3. Deploying to a Cloud Server
Setting up a DigitalOcean droplet.
Configuring SSH keys and creating a non-root `sudo` user.
Installing Docker and pulling the Docker image from Docker Hub.
Running the application using Docker Compose.

4. Configuring a Domain and Making the App Public
Setting up a domain name on Namecheap and pointing it to the server.
Allowing traffic on port 9000 and configuring Nginx as a reverse proxy.
Securing your API with SSL using Certbot.

5. Testing and Monitoring
Ensuring the model is accessible via a public URL.
Monitoring logs and troubleshooting deployment issues.

Why Choose This Course?
Hands-On Deployment: Get real-world experience by deploying a machine learning model end-to-end.
Industry Best Practices: Learn how professionals deploy and maintain ML applications in production.
Clear and Practical Instructions: Follow structured steps with explanations tailored for both beginners and experienced developers.
Build Your Portfolio: Showcasing a deployed project is a valuable asset for job applications and freelance work.

Who Is This Course For?
Data scientists looking to deploy their models.
Developers interested in cloud deployment and containerization.
AI and ML enthusiasts who want to go beyond training models.
Anyone interested in making machine learning applications accessible to users.

Important Links
Lesson One Guide: https://github.com/yacooti/cost-of-li...
Deployment Guide: https://github.com/yacooti/cost-of-li...

Chapters
00:00 Introduction
01:10 Prerequisites
08:54 Setting Up the Project
23:24 Creating Docker Config Files
29:28 Building the Docker Image
35:10 Pushing Image to Docker Hub
37:26 Setting Up the Cloud Server
52:50 Cloning the Project To the Cloud
58:51 Installing Docker On The Server
01:04:23 Configuring The Domain Name
01:07:44 Exposing The App Publicly
01:14:03: Securing The Deployment With SSL
01:18:24 Testing The Deployment
01:22:44 Conclusion

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Website - https://www.yacooti.com

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