Engineering

Thoughts directly from the engineering team at Feature Labs

Engineering Featured

Introducing Featuretools SQL - Automatically Import Relational Data into Featuretools

Deep Feature Synthesis (DFS), the core feature generation algorithm behind our Featuretools library, operates on relational data to generate complex, stacked features that are often highly predictive in machine learning models. This relational data is frequently stored as tables in relational database management systems (RDBMS’s) like Postgres and MySQL.

Engineering

Taming unruly Terraform

Terraform has gained widespread popularity since being first released in 2014, and for a good reason. It is a fantastic open-source tool that allows you to manage and automate infrastructure changes as code across all popular cloud providers. At Alteryx Auto Insights, we use Terraform to manage our cloud environments.

Engineering

Choosing an API framework for your TypeScript backend

If you’re a backend developer, you’re probably aware of Node.js. If you’re not, it’s a run-time environment that includes everything required to execute a program written in JavaScript. It’s both efficient and scalable, which has made it one of the best ways to develop

Engineering

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.

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

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.

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 Featured

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 Spark Apache 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

Engineering

Scaling Featuretools with Dask

How to scale automated feature engineering using parallel processing When 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,

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 Featured

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.

Engineering Featured

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 Featured

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.

Engineering

Bringing data science automation to the domain expert’s playground

In July, we visited Carnegie Mellon University to participate in the 17th annual Simon Initiative’s LearnLab Summer School on Educational Data Mining. During the program, we gave a talk on feature engineering and helped teams complete projects using Featuretools, an open source library for automated feature engineering. Introducing and

Engineering Featured

Open Sourcing Featuretools

I created Deep Feature Synthesis two years ago while I was a student at MIT. My intention from the very beginning was to one day share that technology with the world. That day has finally come, and Featuretools [https://www.featuretools.com/?__hstc=204809999.d7ea11725265176c25a2ba8c7ce2fa86.1513016275744.1513267639349.1513273501689.18&__hssc=