Description:

Docker has become a standard tool for developers around the world to deploy applications in a reproducible and robust manner. The existence of Docker and Docker Compose have reduced the time needed to set up new software and implement complex technology stacks for our applications. There are thousands of tutorials and getting started documents for those wanting to adopt Docker for application deployment.

However, if you’re a data scientist, a researcher or someone working on scientific computing wanting to adopt Docker, the story is quite different. If you work on data science, machine learning or scientific computing, this talk is for you. We'll cover best practices when building Docker containers for data-intensive applications, from optimizing your image build to ensuring your containers are secure and efficient deployment workflows. We’ll talk about the most common problems faced while using Docker with data-intensive applications and how you can overcome most of them. Finally, I'll give you some practical and useful tips to improve your Docker workflows and practices.

Start: Wednesday, 11 May 2022 @ 06:30

End: Wednesday, 11 May 2022 @ 07:00

Timezone: Melbourne

Eligibility:
  • Open to all

Organiser: DockerCon 2022

Contact: Speaker: Tania Allard (Co-director Quansight) https://www.linkedin.com/in/taniaallard

Host institution: None

Keywords: python, data science, Docker

Event type:
  • Webinar
  • Conference
Using Python and Docker for Data Science and Scientific Computing https://dresa.org.au/events/using-python-and-docker-for-data-science-and-scientific-computing Docker has become a standard tool for developers around the world to deploy applications in a reproducible and robust manner. The existence of Docker and Docker Compose have reduced the time needed to set up new software and implement complex technology stacks for our applications. There are thousands of tutorials and getting started documents for those wanting to adopt Docker for application deployment. However, if you’re a data scientist, a researcher or someone working on scientific computing wanting to adopt Docker, the story is quite different. If you work on data science, machine learning or scientific computing, this talk is for you. We'll cover best practices when building Docker containers for data-intensive applications, from optimizing your image build to ensuring your containers are secure and efficient deployment workflows. We’ll talk about the most common problems faced while using Docker with data-intensive applications and how you can overcome most of them. Finally, I'll give you some practical and useful tips to improve your Docker workflows and practices. 2022-05-11 06:30:00 UTC 2022-05-11 07:00:00 UTC DockerCon 2022 None Speaker: Tania Allard (Co-director Quansight) https://www.linkedin.com/in/taniaallard [] [] webinarconference open_to_all pythondata scienceDocker