I’ve always been amazed by how programming extends human capabilities, which was part of what inspired me to pursue computer science. When used carefully and responsibly, machine learning can enable humans even further, amplifying what we can do in fascinating ways. I was excited to talk at our Virtual

Finding out that an application is running out of memory is one of the worst realizations a developer can have. Memory problems are hard to diagnose and fix in general, but I’d argue it’s even harder in Python. Python’s automatic garbage collection makes it easy to get

We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

2020 has been an unprecedented year and Alteryx’s internship and co-op program is no exception.

A normalized, relational dataset makes it easier to perform feature engineering. Unfortunately, raw data for machine learning is often stored as a single table, which makes the normalization process tedious and time-consuming.

Cheers to a fun year helping data scientists and developers build better machine learning models with automated feature engineering. Here's 2018 by the numbers.

How to train, tune, and validate a machine learning model

How to Extract Features from Raw Data for Machine Learning

An explanation and implementation of the first step in solving problems with machine learning.

A General-Purpose Framework for Defining and Solving Meaningful Problems in 3 Steps.

Distributed feature engineering in Featuretools with SparkApache Spark is one of the most popular technologies on the big data landscape. As a framework for distributed computing, it allows users to scale to massive datasets by running computations in parallel either on a single machine or on clusters of thousands of

How to scale automated feature engineering using parallel processingWhen a computation is prohibitively slow, the most important question to ask is: “What is the bottleneck?” Once you know the answer, the logical next step is to figure out how to get around that bottleneck. Often, as we’ll see, the

Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage There are few certainties in data science — libraries, tools, and algorithms constantly change as better methods are developed. However, one trend that is not going away is the move towards increased levels