Uber Machine Learning Blog

At Uber I was the founding product manager for Ubers machine learning platform Michelangelo. Uber ATG developed a model life cycle for quick iterations and a tool for continuous delivery and dependency management.

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At Uber our contribution to this space is Michelangelo an internal ML-as-a-service platform that democratizes machine learning and makes.

Uber machine learning blog. As machine learning continues to evolve transforming the various industries it touches it has only begun to inform the world of audit. As a data scientist and former CPA Auditor I can understand why this is the case. Applications range from model-based simulation and time series forecasting to Bayesian optimization and automatic feature selection.

Introduction In 2016 we published blog posts I II about Schemaless - Uber Engineerings Scalable Datastore. Intuitively most everyone understands that bigger models require large amounts of resources large datasets computational firepower etc but cost is just one piece of MLs scale problem. Our world-class team at Uber AI Engagements connects cutting-edge models in machine learning to the broader business.

Blog How scientists at Uber use Comet to manage machine learning experiments Words by Gideon Mendels April 6 2021 Scale is an interesting often over-simplified challenge in machine learning. November 10 2017. And so in this blog I will give you a bit of a survey of the machine learning approaches Uber used to address the problem of forecasting.

Uber Engineering is committed to developing technologies that create seamless impactful experiences for our customers. In recent months Uber Engineering has shared how we use machine learning ML artificial intelligence AI and advanced technologies to create more seamless and reliable experiences for our users. AI Labs ATG Research Machine Learning these are the three sections of the Uber blog.

It also provides a centralized place to implement many high-level responsibilities including routing protocol conversion rate limiting load shedding. Click to see our publications. Scale is an interesting often over-simplified challenge in machine learning.

This is the paper behind their Michelangelo platform. As the product manager I was responsible for understanding Ubers business needs crafting a practical but ambitious vision for how machine learning can transform decision-making across Uber and defining and managing a machine learning platform. Our machine learning platform Michelangelo lets teams across the company train evaluate and deploy models that help us forecast a wide range of business metrics.

From introducing a Bayesian neural network architecture that more accurately estimates trip growth to our real-time features prediction system and even our own internal ML-as-a-service platform Michelangelo these two fields are critical to supporting Uber. They are pretty. An API gateway provides a single point of entry for all our apps and provides an interface to access data logic or functionality from back-end microservices.

Before you go ahead if you think this is too long to read please use this texttovoice. Machine-learning research ai deep-learning uber deep-reinforcement-learning artificial-intelligence Jupyter Notebook 33 197 2 0 Updated May 25 2020 Synthetic-Petri-Dish. September 5 2017.

It not only enables services such as ridesharing destination prediction driver-rider pairing ETA prediction etc but financial planning and other core business needs. We are increasingly investing in artificial intelligence AI and machine learning ML to fulfill this vision. Your Tech News Blog Friday April 23 2021 How Uber manages Machine Learning Experiments with Cometml At Uber where ML is fundamental to most products a mechanism to manage offline experiments easily is needed to improve developer velocity.

How Uber implements Machine Learning Operations MLOps According to their engineering blog Machine learning helps Uber make data-driven decisions. API gateways are an integral part of microservices architecture in recent years. First of all one should realize that at Uber times series are completely and utterly ubiquitous.

Machine learning helps Uber make data-driven decisions which not only enable services such as ridesharing but also financial planning and other core business needs. Managing multiple machine learning models to enable self-driving vehicles is a challenge.

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