Join Jonathan Fernandes for an in-depth discussion in this video Next steps, part of Docker for Data Scientists.
- [Jonathan] Now that you know the basics of Docker, … go ahead and put a Docker file together. … Create an image and push it to Docker Hub … so that you can share your application with others … or are you working with other teams … that don't have data science and machine learning software … on their computers? … Well, get them to install Docker … and then share analysis and insights with them … without having to worry … about it not working on their computers. … In the past, you might have had to get the help … of data engineers to productionize your code … or make it available before speaking to the business teams. … Now, with Docker, you can turn around applications … and get feedback from stakeholders much more quickly. … This is just the start, … but Docker is key to machine learning and data science. … I hope you found this course helpful. … Now, if you've liked this one, you'll probably like … some of my other courses on LinkedIn Learning. … Thanks for watching, and I'd love to hear back from you …
- 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