distributed computing

OpenTeams Partner Spotlight E01: Coiled

In this webinar, we’ll dive into the challenges of distributed computation for organizations: parallel libraries, such as Dask, are only useful if you both have access to parallel hardware, and the DevOps expertise to use it. This excludes many important communities.

Creating a custom software environment with Coiled

Scalable Python Deployments as a Service

James Bourbeau, Dask maintainer and software engineer at Coiled, recently joined us for a Science Thursday session on “Scalable Python Deployments as a Service”.  In this post, we summarize the key takeaways from the stream. We’ll cover:  A brief overview of Dask  An introduction to Coiled and its offerings  Spinning up a cluster on AWS …

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Prefect logo with slogan "The new standard in dataflow automation."

Dataflow Automation with Prefect and Dask

Our first #ScienceThursday was so much fun we can’t wait to do it again this week! And we’re excited to announce that our good friends at Prefect will be joining to show us how they leverage Dask for their modern workflow orchestration system: Prefect was built to help you schedule, orchestrate and monitor your data …

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An eye in focus with the rest of the image out of focus.

Distributed Data Science for IT Professionals

Scaling Data Science is a Team Sport An increasing number of organizations need to scale data science to larger datasets and larger models. However, deploying distributed data science frameworks in secure enterprise environments can be surprisingly challenging because we need to simultaneously satisfy multiple sets of stakeholders within the organization: data scientists, IT, and management. …

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A diagram showing how your cluster is not a laptop.

Distributed Computing for Data Scientists

An increasing number of organizations need to scale data science to larger datasets and larger models. However, deploying distributed data science frameworks in secure enterprise environments can be surprisingly challenging because we need to simultaneously satisfy multiple sets of stakeholders within the organization: data scientists, IT, and management.

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