Alteryx Innovation Labs

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

Engineering

Why Automated Feature Engineering Will Change the Way You Do Machine Learning

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

Engineering

What Is Machine Learning 2.0?

As the demand from businesses to leverage machine learning continues growing at an exponential rate, the current time-intensive process that heavily relies on highly-skilled ML experts won’t suffice.

Research

Machine learning 2.0 - Engineering data driven AI products

A paradigm shift from the current practice of creating machine learning models.

Leadership

Putting Machine Learning to Work

We started Feature Labs with a straightforward goal: build products that enable any organization to create and deploy machine learning solutions. In 2015 while working in the MIT Computer Science and Artificial Intelligence Lab, my co-founders and I developed an automated data science system that outperformed 615 of 906 top

Engineering

Deep Feature Synthesis: How Automated Feature Engineering Works

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for

Engineering

Feature Engineering vs Feature Selection

All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas have different objectives. Knowing these distinct goals can tremendously improve your data science workflow and pipelines. This

Leadership

Feature Engineering: Secret to data science success

Feature engineering is challenging because it depends on leveraging human intuition to interpret implicit signals in datasets that machine learning algorithms use. Consequently, feature engineering is often the determining factor in whether a data science project is successful or not.

Use Cases

Applying Data Science Automation to Better Predict Credit Card Fraud

An industry report from 2015 found that one out of every six legitimate cardholders experienced at least one declined transaction because of inaccurate fraud detection in the past year.