Docker for Data Engineers

Contents

• Introduction Introduction

  • What is Docker, and how does it help?

    5m 47s
  • Course topics

    1m 54s
  • Prerequisites

    50s
• 1. Introducing Docker 1. Introducing Docker

  • Terms and concepts

    3m 45s
  • Drawbacks of application development on bare metal and VMs

    5m 1s
  • (Locked)
    Introducing containers

    5m 40s
  • (Locked)
    Introducing Docker

    3m 1s
  • (Locked)
    Introducing images

    5m 53s
  • (Locked)
    Building and accessing images

    6m 32s
  • (Locked)
    Microservices

    2m 43s
  • (Locked)
    Containers and kernels

    2m 59s
• 2. Working with Docker 2. Working with Docker

  • (Locked)
    Installing Docker Desktop on Windows

    4m 18s
  • (Locked)
    Installing Docker Desktop on macOS

    2m 10s
  • (Locked)
    Exploring Docker Hub

    4m 18s
  • (Locked)
    Running your first container using docker run

    4m 29s
  • (Locked)
    Pulling images using docker pull

    4m 14s
  • (Locked)
    Viewing a running container on Docker Desktop

    2m 39s
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    Removing containers and images

    2m 30s
• 3. Building Images and Running Containers 3. Building Images and Running Containers

  • (Locked)
    Building your first Docker image

    5m 46s
  • (Locked)
    Overriding the CMD instruction

    1m 52s
  • (Locked)
    Using the Python base image

    5m 18s
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    Running a container in interactive mode

    2m 29s
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    Specifying the Dockerfile to run a Python application

    6m 36s
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    Running the containerized Python application

    5m 7s
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    Running multiple containers from the same image

    3m 28s
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    Building an image that runs a setup script

    6m 14s
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    Overriding the ENTRYPOINT directive

    4m 38s
• 4. Pushing Images to and Pulling Images from Docker Hub 4. Pushing Images to and Pulling Images from Docker Hub

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    Pushing images to Docker Hub

    6m 8s
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    Pushing images to Docker Hub using Docker Desktop

    1m 35s
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    Pulling custom images from Docker Hub

    4m 11s
• 5. Deploying a Containerized Model to Azure Container Instances 5. Deploying a Containerized Model to Azure Container Instances

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    Azure Container Instances

    1m 34s
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    Training an ML model and serializing to a pickle file

    5m 7s
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    Setting up the Dockerfile for model prediction

    5m 45s
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    Running a containerized app locally for churn prediction

    2m 23s
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    Authenticating to Azure using the Azure CLI

    1m 35s
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    Creating an Azure Container Registry and pushing the image

    3m 58s
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    Deploying a container to Azure Container Instances

    4m 9s
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    Making predictions using the containerized application

    2m 44s
• Conclusion Conclusion

  • (Locked)
    Summary and next steps

    1m 15s