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Docker Masterclass for Machine Learning and Data Science

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Docker Masterclass for Machine Learning and Data Science - Learn how to containerize and deploy your ML projects with Docker
  • Created by Jordan Sauchuk, Ligency Team
  • English [Auto]

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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.

Description

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!

So, are you ready to dockerize your ML projects? Enroll now!

See you in the classroom.

Who this course is for:

Anyone interested in advancing their careers and obtaining a skill that not only is highly relevant now, but will only continue to grow.

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