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Random Forest on GPUs: 2000x Faster than Apache Spark

This blog post compares using RAPIDS and Dask vs Apache Spark for model training

Supercharging Hyperparameter Tuning with Dask

The distributed computing framework Dask is great for hyperparameter tuning, since you can train different parameter sets concurrently.

Practical Issues Setting up Kubernetes for Data Science on AWS

Data science has unique workflows that don't always match those of software engineers and require special setup for Kubernetes.

Setting Up Your Data Science & Machine Learning Capability in Python

Python is a great language to base your DS/ML framework on, and allows you to avoid being locked into one vendor specific framework.

Snowflake and Dask

This article covers efficient ways to load data from Snowflake into a Dask distributed cluster.

Should I Use Dask?

It's not always clear when using the distributed framework Dask is the right choice.

3 Ways to Schedule and Execute Python Jobs

Being able to run a Python script on a schedule is an important part of many data science tasks. This blog post walks through three …

A Guide to Convolutional Neural Networks — the ELI5 way

Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. …

How to Set a Default Environment for Anaconda and Jupyter

Learn how to set a default environment for your Anaconda and Jupyter workflows for a seamless and streamlined data science experience.