featuretools

As another option, Compose is a ML tool for labeling data. It structures the labeling process and integrates easily with Featuretools which automates feature engineering. Definitely worth checking out! [1] https://github.com/FeatureLabs/compose [2] https://github.com/FeatureLabs/featuretools

I know I'm not the only one, but it's hard to imagine doing my job the last several year without Pandas. Even though Pandas has been used in production by many people as basically a 1.0.0 release for a long time, this an amazing milestone and I think everyone in my office smiled when they saw the release news. I think it's worth it to acknowledge the great stewardship of the community by all the Pandas developers (and the rest of people in the PyData ecosystem). It has been an inspiration for me as I create and contribute to open source libraries for data science [0][1]. [0] https://github.com/FeatureLabs/featuretools/ [1] https://github.com/FeatureLabs/compose/

Part of the problem is that if you gave 20 developers/data scientists/ml engineers the same the set of data and asked them the do data prep and feature engineering, you'd probably have them come back with 20 different approaches. To avoid pipeline jungles, teams need to agree to certain API's that their data processing code will follow e.g scikit-learn helped many people standardized around fit/predict/transform for their machine learning algorithms. In the future, I expect we'll see this expand to other parts of the process, such as feature engineering. Towards that goal, I work on an open source library trying to do this for feature engineering called Featuretools. You can check it out here: https://github.com/FeatureLabs/featuretools/

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | https://www.featurelabs.com At Feature Labs, we’re building automation technology to make machine learning easier to use. We work on many unique and important machine learning challenges with a focus on making sure machine learning isn’t just a theoretical endeavor but has a real-world impact. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. We love open source and hope you do too: https://www.featurelabs.com/open Here is one of our most popular libraries: https://github.com/featuretools/featuretools Apply at https://www.featurelabs.com/careers/