DevOps for Data Scientists: Containers for Data Science
DevOps
| Intermediate
- 16 videos | 58m 13s
- Includes Assessment
- Earns a Badge
In this 16-video course, explore the use of containers in deploying data science solutions by using Docker with R, Python, Jupyter, and Anaconda. Begin with an introduction to containers and their use for deployment and data science. Then examine approaches to infrastructure as code for data deployment, and concepts behind Ansible and Vagrant approaches to data science deployment. Explore the main features of provisioning tools used in data science. You will learn how to use Docker to build data models, then use it to perform model testing for deployment, to manage R deployments, and for a PostgreSQL deployment. Also, discover how to use Docker for persistent volumes. Next, learners look at using Jupyter Docker Stacks to get up and running with Jupyter and using the Anaconda Distribution to run a Jupyter Notebook. This leads into using Jupyter Notebooks with a Cookiecutter data science project. Then learn about using Docker Compose with PostgreSQL and Jupyter Notebook, and using a container deployment for Jupyter Notebooks with R. The concluding exercise involves deploying Jupyter.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this course
Describe the use of containers for data science
Describe approaches to infrastructure as code for data deployment
Describe ansible and vagrant approaches to data science deployment
Describe provisioning tools used in data science
Use docker to build a data model
Use docker to perform model testing for deployment
Use docker to manage r deployments -
Use docker for a postgresql deployment
Create a docker persistent volume
Use jupyter docker stacks to get up and running with jupyter
Use the anaconda distribution to run a jupyter notebook
Use jupyter notebooks with a cookiecutter data science project
Use docker compose with postgresql and jupyter notebooks
Use a container deployment for jupyter notebooks with r
Use a container strategy for a jupyter deployment
IN THIS COURSE
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1m 22s
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4m 37s
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3. Infrastructure as Code for Data3m 14s
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4. Ansible and Vagrant for Data3m 49s
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5. Provisioning for Data Science2m 58sUpon completion of this video, you will be able to describe provisioning tools used in data science. FREE ACCESS
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6. Using Docker for Data Science5m 3sDuring this video, you will learn how to use Docker to build a data model. FREE ACCESS
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7. Deploying Docker Images for Data Science3m 11sIn this video, you will learn how to use Docker to test models for deployment. FREE ACCESS
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8. Docker for R4m 24sFind out how to use Docker to manage R deployments. FREE ACCESS
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9. Docker for SQL3m 21sDuring this video, you will learn how to use Docker to deploy PostgreSQL. FREE ACCESS
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10. Docker Persistent Volumes4m 21sIn this video, you will create a Docker volume that persists. FREE ACCESS
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11. Using Jupyter Docker Stacks2m 37sLearn how to use Jupyter Docker Stacks to get Jupyter up and running. FREE ACCESS
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12. Anaconda Packages for Jupyter3m 16sIn this video, you will learn how to use the Anaconda distribution to run a Jupyter Notebook. FREE ACCESS
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13. Cookiecutter Data Science with Jupyter2m 44sIn this video, you will use Jupyter Notebooks with a Cookiecutter data science project template. FREE ACCESS
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14. Docker Compose for Data Science5m 20sIn this video, learn how to use Docker Compose with PostgreSQL and Jupyter Notebooks. FREE ACCESS
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15. Using R in Jupyter with Docker4m 20sIn this video, you will deploy a container for Jupyter Notebooks with R. FREE ACCESS
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16. Exercise: Deploying Jupyter3m 36sIn this video, find out how to use a container strategy for deploying Jupyter. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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