Any data scientist will tell you that data wrangling is most of the battle when it comes to Machine Learning. Your business may have a […]
At Vidora, our goal is to reduce the bottlenecks in machine learning deployment and eliminate a lot of the data wrangling required with end-to-end machine […]
Complex Feature Engineering with a Simple UI Feature engineering for machine learning problems is often deemed a mysterious art. Yet, it remains one of the […]
Are your Machine Learning efforts slowed down by data wrangling? Vidora’s Machine Learning (ML) Pipeline technology automates aspects of data wrangling including pre-processing, feature cleaning, […]
Earlier this year, the Vidora product team announced the launch of prediction scheduling in Cortex. By setting up any Cortex pipeline to regenerate predictions on […]
Understanding the health of your customer base is critical information for any business. As a result, marketers are constantly looking for ways to effectively predict […]
Vidora Cortex is the first and only Machine Learning platform which automates the entire Machine Learning (ML) Pipeline from start to finish, including feature cleaning […]
Vidora announced the launch of Understandable ML today (here is a link to the press release), providing unprecedented insight into complex machine learning models, and […]
Today Vidora launched a set of Pipeline Performance Graphs built on top of Cortex’s new Automated Pipeline Testing framework. Traditionally, ML performance metrics are too […]
Data is changing the game for marketers. In today’s age of ubiquitous digital content, customers are bombarded with generic marketing messages and advertisements everywhere they […]
Today, Vidora released ML Pipeline Management within Cortex, our self-service Machine Learning platform. As enterprise organizations increasingly deploy Cortex across their businesses, it’s important that […]
Vidora empowers anybody to drive towards their most important organizational goals using Machine Learning. Cortex enables anyone to build and deploy predictive models with just […]