machine learning

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.

Dask logo with matrix background

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!

The Future of Distributed Machine Learning

We recently chatted with Andy Müller, core developer of scikit-learn and Principal Research Software Development Engineer at Microsoft. Andy is one of the most influential minds in data science with a CV to match. He shares his thoughts on distributed machine learning with open-source tools like Dask-ML as well as proprietary tools from the big cloud providers. …

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Florian Jetter, Sr Data Scientist at Blue Yonder, joins Matt Rocklin and Hugo Bowne-Anderson to discuss supply chain analytics at scale.

Data Processing at Blue Yonder

Florian Jetter, Sr Data Scientist at Blue Yonder, joins Hugo Bowne-Anderson and James Bourbeau to discuss supply chain analytics at scale. Blue Yonder provides software-as-a-service products around supply chain management. Along such a supply chain there are billions of billions of decisions to be made, how much to order, when to ship products, how much …

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A look at commuter data in Kansas City

Large Scale Machine Learning for Urban Planning

The Coiled team was recently joined by Brett Naul, founding engineer at Replica, where we discussed large-scale machine learning and travel simulations for urban planning. During this session, we learned more about: Interactive products for urban planning, Building synthetic populations from large data sets like the US census, Data engineering workflow with Dask and Prefect, …

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green and red light wallpaper

Do we really need distributed machine learning?

We recently chatted with Andy Müller, core developer of scikit-learn and Principal Research Software Development Engineer at Microsoft. Andy is one of the most influential minds in data science with a CV to match. He shares his thoughts on distributed machine learning with open-source tools like Dask-ML as well as with proprietary tools from the …

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Scalable Python Deployments as a Service

James Bourbeau, Dask core contributor and maintainer who works at Coiled building tools for scalable computing, joins Hugo Bowne-Anderson to discuss and code about scalable data science deployments as a service and how he thinks about these things at Coiled.  Coiled Cloud is an opinionated deployment-as-a-service product/library for scaling Python data science and machine learning …

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Large-Scale Machine Learning for Urban Planning

Brett Naul, founding engineer at Replica, joins Matt Rocklin and Hugo Bowne-Anderson to discuss large-scale machine learning and travel simulations for urban planning. Replica uses Dask to easily scale travel simulations to hundreds of millions of agents on Google Container Engine. The rich Python data science and statistical ecosystems make it easy to build new …

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A four-quadrant graph with model size on the y-axis and data size on the x-axis.

Big Data vs. Big Model: Scaling Your ML Workflow

Tom Augspurger, Data Scientist at Anaconda and lead maintainer of Dask-ML, recently joined us to discuss how he likes to think about scalable machine learning in Python. As Tom shared with us on the live stream, “You have your machine learning workflow that works well for small problems. Then there are different types of scaling …

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