Docker for Data Scientists

Docker for Data Scientists
Docker for Data Scientists
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 46m | 133 MB

In a field where reproducible results are essential, Docker is rapidly emerging as one of the top tools for bringing efficiency to the work that data science teams—particularly those working in machine learning (ML)—are doing. Creating and developing ML models is often messy. Seasoned data scientists know that different versions of the same software can produce different results. With Docker, you can include the right versions of each needed dependency and library, so no one ever has to do any configuration. After the Dockerfile is built, you’ll have exactly what you need. In this course, Jonathan Fernandes helps data scientists get up and running with Docker, demonstrating how to build a Dockerized ML application that can easily be shared. Along the way, he shares common use cases for the tool. Upon wrapping up this course, you’ll be prepared to leverage the power of containers in your other ML projects.

Topics include:

  • Why Docker is gaining prominence
  • Running a container
  • Docker under the hood
  • Working with Dockerfiles
  • Uploading images to Docker Hub
  • Common use cases for Docker
Table of Contents

Introduction
1 Docker and data science
2 What you should know

Introduction to Docker
3 What is Docker
4 Why Docker
5 Install Docker
6 Install a text editor

Working with Docker
7 Using images
8 Running a container
9 Docker under the hood

Working With Dockerfiles
10 Dockerfile basics
11 Troubleshooting Dockerfiles
12 Uploading images to Docker Hub

Common Use Cases
13 Creating a common development environment
14 Sharing results with colleagues

Conclusion
15 Next steps