- With machine learning residing at the heart of our new understanding of Artificial Intelligence (AI) as it does, there is (arguably) a real need for IT departments to be able train, tune, test and deploy the predictive models they are using to create what we call ‘automation intelligence’ and make AI for business happen.
- The firm insists that most enterprises currently trying to use AI and machine learning automation usually struggle to put predictive models to work because data professionals often operate in silos and the workflow — from data preparation to updating models — ends up creating bottlenecks.
- As an answer then, Pentaho’s Data Integration and analytics platform aims to end the ‘gridlock’ associated with machine learning by enabling smoother team collaboration.
- With machine learning packages like Spark MLlib and Weka, Pentaho says it allows data scientists to train, tune, build and test models faster.
- With each train carrying thousands of sensors generating huge amounts of data per day, the project’s data engineers and scientists face many challenges associated with big data and machine learning.
@TamaraMcCleary: “How To Train A Machine Brain, Pentaho’s 4 Pillars Of #AI” open tweet »