How Uber Uses XGBoost

The article discusses how Uber enhanced its Guidance Heatmap using deep probabilistic models to provide drivers with better insights into potential earnings.

This article discusses how Uber has integrated explainability into its machine learning platform, Michelangelo, using Integrated Gradients (IG) to provide interpretable attributions for deep learni...

This article discusses how Uber enhances personalized CRM communication using contextual bandit strategies, particularly focusing on the application of AI/ML techniques to optimize email content.

The article discusses how Uber utilizes Ray®, a general compute engine for Python®, to enhance the efficiency of its rides business through improved machine learning model performance and optimizat...

The article discusses Uber's evolution in machine learning (ML) through its centralized platform, Michelangelo, highlighting its transition from predictive to generative AI.

The article discusses how Uber optimizes the timing of push notifications using machine learning and linear programming.

The article discusses Uber's approach to automating offline inferences using machine learning and natural language processing on support interaction data.

The article discusses DeepETA, Uber's advanced model for predicting arrival times using deep learning techniques.

The article discusses various strategies for tuning machine learning model performance at Uber, focusing on hyperparameter optimization, feature transformation, and the use of learning curves.

The article discusses the integration of Elastic Distributed Training with XGBoost on Ray, highlighting how this approach addresses challenges in distributed machine learning at scale.

The article discusses Uber Freight's innovative approach to freight pricing using a Controlled Markov Decision Process (MDP).

The article discusses the integration of Elastic Horovod with Ray, focusing on how this combination enhances distributed deep learning training by enabling autoscaling and fault tolerance.

The article discusses the challenges and solutions involved in productionizing distributed XGBoost for training deep tree models on large datasets at Uber.

The article features an interview with Felix Cheung, Data Platform Engineering Manager at Uber, discussing the advantages of open source software in private enterprise.

The article discusses the modeling of censored time-to-event data using Pyro, an open-source probabilistic programming language.

The article introduces Michelangelo PyML, Uber's platform designed for rapid Python machine learning model development.

The article introduces Michelangelo, Uber's internal machine learning platform designed to democratize machine learning and streamline the process of building, deploying, and operating ML solutions...