When data-driven organizations start using Machine Learning at scale (i.e., automatically creating and operating machine-learned models for dozens of different use cases), they soon realize how important it is to leverage a robustly-engineered platform that removes the complexities of Machine Learning and allows different stakeholders to focus on what matters most; enhancing and automating decision making. In this workshop, we will give you an extensive overview of the BigML platform and discuss the design principles that have been followed to remove the extra complexities that developing end-to-end Machine Learning applications typically implies, while ensuring traceability, reproducibility, and many other features that mitigate the risks of operating hundreds, thousands or even millions of machine-learned models in the real world.
Speakers: Poul Petersen, Chief Infrastructure Officer; and Greg Antell, Machine Learning Architect.
When data-driven organizations start using Machine Learning at scale (i.e., automatically creating and operating machine-learned models for dozens of different use cases), they soon realize how important it is to leverage a robustly-engineered platform that removes the complexities of Machine Learning and allows different stakeholders to focus on what matters most; enhancing and automating decision making. In this workshop, we will give you an extensive overview of the BigML platform and discuss the design principles that have been followed to remove the extra complexities that developing end-to-end Machine Learning applications typically implies, while ensuring traceability, reproducibility, and many other features that mitigate the risks of operating hundreds, thousands or even millions of machine-learned models in the real world.
Speakers: Poul Petersen, Chief Infrastructure Officer; and Greg Antell, Machine Learning Architect.
