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.
At Alteryx, we aim to 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
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
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
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
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
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