Predicting Taxi Fare in New York

Presented by

Bart Koek, Solution Architect at Dataiku

About this talk

Today, the benefit of Machine Learning is conditioned to its deployment in real-time. In this talk, Bart will explain how to deploy a real-time taxi fare prediction engine to power an Uber-like application. Along the cycle of developing such project, we will highlight key lessons we learned: - Understand the problem before building models - Do not add features for the sake of features - Try as many algorithms as possible - Simplify your pipeline before deployment Bart Koek, Solution Architect at Dataiku: Bart is in charge of helping companies in the Nordics region and across Europe to address, implement, and deploy data-driven projects. Dataiku is a collaborative data science platform, which integrates all the capabilities required to build end-to-end highly specific services that turn raw data into business-impacting predictions quickly.

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Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data. More than 450 companies worldwide use Dataiku to systemize their use of data and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between.