Vidora CTO Describes Implementing the Machine Learning Pipeline
Last month, Vidora held ‘The Future of Machine Learning Business’ event in conjunction with News Corp. At the event where we discussed where the machine learning space is headed. We also talked about the steps involved in implementing the machine learning pipeline, where the latest innovations are most likely to come from, and how media businesses such as News Corp are thinking about ML and planning for its future.
Part One of the event was a talk by Vidora CTO Abhik Majumdar, PhD on the future of machine Learning in business. In this clip, Abhik describes the various steps required when implementing the machine learning pipeline. He also shows how deploying ML algorithms within businesses is much more difficult than just choosing the best neural network model. In particular, he illustrates through examples that the hardest parts of machine learning actually lie in the initial steps of the pipeline, such as feature cleaning and feature engineering. It is in these areas where Vidora believes the biggest innovations within ML will take place over the coming years.
Vidora enables anyone in any business to build and use complex machine learning models. With Vidora’s self-service platform, Cortex, machine learning is intuitive, interpretable and fast. Cortex also automates the entire machine learning pipeline from raw data to model outputs. Experts in machine learning and artificial intelligence from Stanford, Berkeley and Caltech developed Cortex. Finally, Cortex sits at the heart of some of the largest global brands, such as Walmart, News Corp, and Discovery.