Luke Arrigoni started Arricor in 2012 to help large companies make sense of their data. Since then, he and the team have taught organizations like Goldman Sachs, AT&T, and Thomson Reuters about the principles of AI. His secret? Focus on the business problem and the right technology approach becomes obvious.
How UPS uses AI to automatically assign the right tax code for packagesWhat responsibility AI developers have for the decisions their algorithms makeHow to clean dirty data to make it ready for AI model training When to use neural nets vs. gradient-boosted treesWhich tasks are good candidates for classifier models vs. NLPWhich job skills are future-proof… and which are likely to be replaced by automation References in this episode:
Fish from Mozart Data on AI and the Future of WorkAirflow for data pipeline automation
