Alteryx Innovation Labs

Type Inference for Natural Language

One step in natural language data analysis is actually recognizing whether or not our data can be defined as natural language.

Introducing Woodwork - An Open Source Python Library for Rich Semantic Data Typing

At Alteryx, we aim create tools for advancing machine learning capabilities. To help everyone solve impactful problems, we're building innovative open source tools for each step of the machine learning pipeline, automating all parts of the machine learning process, and making it easy for anyone to gain insights and build

Detecting and Correcting Irregularly Spaced Time Series Data With EvalML

Many modern machine learning models leverage time series data to predict future outcomes. Instead of utilizing static data to predict a target variable, time series adds the element of chronology. This can be leveraged with any sort of time-based task, such as predicting the temperature tomorrow or future sales growth

Advancing Model Understanding: Human-Readable Pipeline Explanations

EvalML offers a wide variety of ways to understand the machine learning models that you train using our software.

Introducing Featuretools 1.0

Alteryx is excited to introduce the release of Featuretools version 1.0! After years of development, dozens of releases, hundreds of closed pull requests, millions of downloads, and numerous production deployments, we believe Featuretools has earned the “1.0” version label. This release represents not only a significant milestone in

Learn more about EvalML in this podcast

Recently, Angela Lin and Jeremy Shah participated in a Podcast.__init__ episode where they talked about EvalML, an automated machine learning library written in Python. They describe what automated machine learning really is and talk about the use cases EvalML solves, as well as many other topics. You can listen

Engineering

Accomplish More with Automated Machine Learning

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

Engineering

How to troubleshoot memory problems in Python

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

Using Text Data in EvalML with Woodwork

Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.

Engineering

Easy, Open-Source AutoML in Python with EvalML

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.

Engineering

Alteryx Internship Projects 2020

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

Introducing Compose for Prediction Engineering

How to build better training examples in a fraction of the time.

Visualizing Automated Feature Engineering

How Feature Lineage Graphs Can Help to Analyze Generated Features

Leadership

Analyticon: An Inspired Event for Data People

Tackling the greatest global and societal problems can only happen when everyone works together, regardless of programming language, business vertical, or degree.

Encode Smarter: How to Easily Integrate Categorical Encoding into Your Machine Learning Pipeline

Automated feature engineering solves one of the biggest problems in applied machine learning by streamlining a critical, yet manually intensive step in the ML pipeline. However, even after feature engineering, handling non-numeric data for use by machine learning algorithms is unavoidable and presents its own set of unique challenges. This

Engineering

Natural Language Processing for Automated Feature Engineering

How to apply the nlp-primitives library using Featuretools.

Engineering

Automatic Dataset Normalization for Feature Engineering in Python

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.

Engineering

Featuretools Year in Review

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.

Engineering

Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value

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

Engineering

Feature Engineering: What Powers Machine Learning

How to Extract Features from Raw Data for Machine Learning

Engineering

Prediction Engineering: How to Set Up Your Machine Learning Problem

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

Engineering

How to Create Value with Machine Learning

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

Engineering

Featuretools: One year of automating feature engineering

Happy birthday, Featuretools! One year ago, we open sourced Featuretools, making it available to the entire world.

Engineering

Featuretools on Spark

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

Engineering

Scaling Featuretools with Dask

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