Descriptions
Overview
Learn how to containerize and deploy your ML projects with Docker
What you’ll learn:
- Use Docker in order to build containers to deploy applications.
- Learn the intuition behind Docker and understand how it works.
- Learn the ins and outs of working with Docker
- Understand how to create and deploy Docker containers from scratch.
- You will also learn about Docker Images, Docker Hub, security with Docker, and much more so that you can implement Docker as needed throughout your career.
Every data scientist is aware that, at some point or another, they’ll need to show off their progress and results. And there couldn’t be a bigger fear than not having your algorithm run on another computer for reasons you can’t define.
Enter Docker Masterclass for Machine Learning and Data Science. Led by Docker evangelist and Cybersecurity expert Jordan Sauchuk, this course is designed to get you up and running with Docker, so you will always be prepared to ship your content no matter the situation.
You’ll learn the ins and outs of Docker, as well as Docker Swarm, Docker Compose, and using Docker with AWS. Also, your Docker path will cover the following steps:
- A Comprehensive Introduction to Docker
- Getting the Docker basics down
- Using Dockerhub
- Docker Challenges
Embedded in each section you’ll not only find great video lessons, but also enriching articles and quizzes designed to take you to Docker heaven! On top of that, the content is constantly being updated, so be sure to check every so often for new videos!
Once you’re done with the course, you’ll be certain you can take on any Docker challenges and ship your projects like a pro!
Specification of Docker Masterclass for Machine Learning and Data Science
- Publisher : Udemy
- Teacher : Jordan Sauchuk , SuperDataScience Team , Ligency Team
- Language : English
- Level : All Levels
- Number of Course : 84
- Duration : 4 hours and 23 minutes
Content
Requirements
- The requirements for this course is simple. We will be using Docker desktop (available on Windows, Mac, Linux) and other Docker services that we will walk through the sign up process, or related information for. We will also be using Python, and any related libraries. You can use any IDE that you would like, but we will use Spyder as an example in the course when needing to write our code, and the terminal or shell/prompt.
Pictures
Download Links
File size
570 MB
