Uber is one of those organizations that rely heavily on data. Each day, millions of trips take place in 700 cities across the world, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables Uber to deliver a smooth riding experience to its customers. With access to the rich dataset coming from the cabs, drivers, and users, Uber has been investing in machine learning and artificial intelligence to enhance its business. Uber AI Labs consists of ML researchers and practitioners that translate the benefits of the state of the art machine learning techniques and advancements to Uber's core business. From computer vision to conversational AI to sensing and perception, Uber has successfully infused ML and AI into its ride-sharing platform.
Uber expanded Michelangelo "to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow [instead of just using Spark for everything]." So why did Uber (and many other tech companies) build its own platform and framework-independent machine learning infrastructure? The posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ecosystem as a central, scalable, and mission-critical nervous system. It allows real-time data ingestion, processing, model deployment, and monitoring in a reliable and scalable way. This post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers, and production engineers. By leveraging it to build your own scalable machine learning infrastructure and also make your data scientists happy, you can solve the same problems for which Uber built its own ML platform, Michelangelo. Based on what I've seen in the field, an impedance mismatch between data scientists, data engineers, and production engineers is the main reason why companies struggle to bring analytic models into production to add business value.
Uber Engineering formally introduced its internal Machine Learning as a Service platform Michelangelo in a company blog post Tuesday. Uber began building the AI platform with a combination of open-source and in-house components in 2015 and now deploys it across company services such as UberEATs. Michelangelo covers end-to-end ML workflow and allows Uber teams to manage data; teach, evaluate and employ models; and create and track predictions. It also serves deep learning, time series forecasting and other machine learning models, and the company is focusing on improving developer productivity on the platform. Uber is not the only large company creating in-house machine learning platforms tailored to its needs.
Companies of all sizes are not satisfied with their machine learning process and various challenges to widespread adoption remain. SEATTLE, Oct. 16, 2018 (GLOBE NEWSWIRE) -- Algorithmia announces the results of a survey on enterprise machine learning. The comprehensive survey, titled "State of Enterprise Machine Learning," is a first for Algorithmia and was designed to explore the ways in which companies of all sizes are utilizing machine learning. The survey was completed by over 500 data science and machine learning professionals, the majority of whom were based in North America. A report detailing the survey's findings can be foundhere.
"Eight-pack" stomachs carved into two sculptures led researchers to confirm the works as the only known surviving bronzes cast by Michelangelo. A worldwide team led by the University of Cambridge confirmed the attribution after four years of research. The 1m-high (3.2ft) works depict two muscular male nudes riding on panthers. In 2002 they were sold by Sotheby's for £1,821,650, but if the Michelangelo evidence is accepted they could be worth hundreds of millions. Last year, Leonardo da Vinci's Salvatore Mundi sold for $450m (£350m) and confirmed works by Michelangelo would have valuers dreaming of such huge numbers.