Machine Learning in Industrial Machinery: Optimization of Passenger Waiting Time for Elevators
This post is a continuation of our series of blog posts highlighting presentations from the 2nd Edition of Seville Machine
How to Configure Optional Inputs for WhizzML Scripts
WhizzML, the powerful Domain-Specific Language (DSL) developed by BigML, is used not only to automate Machine Learning (ML) workflows but
Door-to-Door Data Delivery with External Data Sources
A common step when working with BigML is extracting data from a database or document repository for uploading as a
Fundación ONCE and BigML Join Forces to Create the World’s First Accessible Machine Learning Platform and Drive New Applications
BigML, the leading Machine Learning platform and Fundación ONCE, a Spanish foundation that has a long history of improving the
Introducing New Data Connectors and BigML Dashboard Enhancements
We’re excited to share that we have just released a trio of new capabilities to the BigMLplatform. In this post,
Claire, the Smart B2B Marketplace for the Food Industry powered by BigML
The food trading industry is one of those green-fields, where digitalization hasn’t really taken off in full force. Against this
Machine Learning in Construction: Predicting Oil Temperature Anomalies in a Tunnel Boring Machine
Today, we continue our series of blog posts highlighting presentations from the 2nd Edition of Seville Machine Learning School (MLSEV).
MLSEV Conference Videos: ‘Six Challenges of Machine Learning’
We really enjoyed virtually hosting thousands of business professionals, developers, analysts, academics, and students during the two jam-packed days of
The 2nd Edition of Seville Machine Learning School gathers thousands virtually
Due to the COVID-19 (Novel Coronavirus) pandemic, we’re living through unprecedented times that have put life on hold practically everywhere
Machine Learning Benchmarking: You’re Doing It Wrong
I’m not going to bury the lede: Most machine learning benchmarks are bad. And not just kinda-sorta nit-picky bad, but catastrophically
