COILED – Burst to the cloud with data science and ML workflows
In this on-demand webinar, Matt Rocklin, CEO of Coiled, explains how they help data scientists use Python for ambitious problems, scaling to the cloud for computing power, ease, and speed—all tuned for the needs of teams and enterprises. Coiled is built on Dask, a free and open-source library that helps scale your data science workflows and provides a complete framework for distributed computing in Python.
You will learn how to use the infinite resources of the cloud for your large-scale data engineering and ML workloads while slashing the time it takes to process your pipelines.
Discover how to scale and distribute your Python code easily with unlimited processing power available in the cloud to accelerate your innovation.
What You’ll Learn
You will learn how to use the infinite resources of the cloud for your large scale data engineering and ML workloads, while slashing the time it takes to process your pipelines.
Coiled is the Dask cloud platform for large scale data workloads.
Dask is the #1 big data Python native platform for general purpose distributed computing.
Matthew Rocklin has a background in physics and holds a PhD in computer science. He has served different roles as developer and solution manager, but is most famous for his work on Dask. He joins us as founder and CEO of Coiled, the scalable Dask-based cloud platform that lets data scientists accelerate computation by parallelizing workloads and taking advantage of GPUs.
Rocklin is also the initial author of Coiled’s underlying technology, Dask. He developed Dask to help customers solve challenging distributed computing problems while working at Anaconda. While he is primarily known for his work on Dask, he also coordinates and maintains several dozen libraries within Python’s numeric computing ecosystem, with a substantial focus on efficient and scalable computing.
Matthew is a frequent speaker at several technical, academic, and industry events, such as PyData, SciPy, Google Next, and The International Conference on Machine Learning.
He has a Doctorate of Philosophy in Computer Science from the University of Chicago, and a Bachelors in Physics, Mathematics, and Astronomy from the University of California.
When not working, Matthew enjoys physical outdoor activities like cycling and rock climbing. He also dabbles in amateur woodworking.