scaling data science

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How to learn Dask in 2020

As is the guiding philosophy behind OSS, Dask is a community-driven project, and the content in this post follows suit. The open-source curriculum below pulls from diverse resources, experts, and platforms to guide you in learning Dask in 2020 via the most straightforward path possible. Enjoy!

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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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Dask logo with matrix background

Coiled’s first live stream… Science Thursdays!

Join us for Coiled’s first live stream! We’ll cover both the opportunities and the challenges of scaling data science workloads with Pandas using a real-world example: Explore and clean a raw dataset with Pandas; Scale this workload locally with Dask; Scale this workload onto the cloud with Dask and Coiled. If you know a bit …

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A Brief History of Dask

Dask, the open source package for scalable data science, was developed to meet the needs of modern data professionals. This post describes the evolution of the Dask project and how it meets the needs of people working with medium-to-large datasets across industries (such as energy, finance, and the geosciences) and basic research (such as astronomy, biomedical imaging, and hydrology).

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Challenges of Scaling Data Science in Enterprise

Summary Deploying data science and machine learning frameworks to data science teams is made complex by organizational constraints like security, observability, and cost management. This post lays out the challenges that arise when exposing scalable computing to data science teams in large institutions, and the enterprise infrastructure necessary to meet those challenges. We then finish …

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