Scalable Machine Learning with Dask

Join us for Coiled’s first machine learning at scale live stream!

Eric Ma, notorious Pythonista and data scientist at Novartis, joins regulars Matt Rocklin and Hugo Bowne-Anderson, to show how Dask and distributed compute allows him to accelerate his science and machine learning by several orders of magnitude.

We’ll cover both the opportunities and the challenges of scaling data science workloads with Dask using the real-world example of building a machine learning model of protein melting points:

  1. Check out how to pass protein sequences through a recurrent neural network (RNN) for machine learning feature engineering (really, we’re not just using buzz terms);
  2. Fit a machine learning model using Dask-accelerated random forests;
  3. See the types of pain points that arise in the process (more on those here).

If you know a bit of machine learning, you’ll learn how to scale up your data work to larger datasets with Dask.

If you’re comfortable with Dask, you’ll see how to seamlessly move from local data analysis to operating in the cloud at scale in a few minutes, making it easier to iterate and innovate: science!

Join us this Thursday, July 16th at 5pm US Eastern time on our YouTube channel as we encounter, triage, and resolve common challenges that come with scaling data science in the real world.

Dask logo with matrix
Share

Sign up for updates