Building intelligent applications, experimental ML with Uber’s Data Science Workbench
•Als PPTX, PDF herunterladen•
5 gefällt mir•524 views
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflows including data exploration, feature engineering, machine learning model training, testing, and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and the ability to share their works through community features. It also has support for notebooks and intelligent applications backed by Spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools. In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies machine learning extensively to solve some hard problems. Some use cases include calculating the right price for a ride for over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, model/feature stores, and other ML tools to realize the vision of a complete ML platform for Uber. Speakers Adam Hudson, Uber, Senior Software Engineer Atul Gupte, Uber, Product Manager
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Building intelligent applications, experimental ML with Uber’s Data Science Workbench
Ähnlich wie Building intelligent applications, experimental ML with Uber’s Data Science Workbench (20)
Mehr von DataWorks Summit
Mehr von DataWorks Summit (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Building intelligent applications, experimental ML with Uber’s Data Science Workbench
- 1. Building Intelligent Applications & Experimental ML with Uber’s Data Science Workbench Adam Hudson & Atul Gupte Uber Inc.
- 2. / Data at Uber / Analytics Stack / Machine Learning at Uber / Data Science Workbench / Real-world Impact Contents
- 3. Engineer turned Product Manager Previously: building FarmVille & the mobile advertising platform @ Zynga Currently: Product Manager for Data Science Workbench & Data Warehouse / About Atul
- 4. / Data at Uber
- 5. Uber's mission is to bring reliable transportation - to everyone, everywhere
- 6. Data informs every decision at the company
- 7. Uber’s massive data holds deep, hidden insights. We surface them
- 8. 6,000+ data scientists, engineers, and operations managers rely on us to support the business
- 9. Data is what differentiates Uber but, data at Uber is unlike anywhere else.
- 10. Delicate marketplace with network effects Bits to atoms Business New LOBs spun up in a snap Pluggable mobility platform Spatio-temporal Analytics Sheer scale Real-time. Real-world. ML is Uber’s brain Apps/Machine generated queries Varied skills: BI to DNN Consumers Internal and external 6,000 and growing What makes Uber unique
- 11. MISSION Move the world with global data, local insights, and intelligent decisions. Data Platform Team
- 12. / Data Analytics Stack
- 13. The Data Team Ingest Workflow Management Store Produce Model Ad-Hoc & Streaming Analytics Business Intelligence Machine Learning Metadata/ Knowledge Experimentation / Segmentation Visualization Data Infrastructure Data Platforms Data Services & Analytics Disperse
- 14. Kafka Schemaless SOA BI Apps Ad-hocExperimentation ML Notebooks Cluster Management All-Active Observability Security Raw Data Raw Tables Hadoop Hive Presto Spark Modeled Tables Vertica Vertica Warehouse AthenaX Apollo Streaming Real-time Metadata/Workflow Management Data Infrastructure
- 15. / Machine Learning At Uber
- 16. The hype ● Ability of a machine to learn without being explicitly programmed ● Identify hidden patterns in the world based on current and historical data and use it to predict the future ● Ability of a machine to get better at a task with data and experience ● Learn from mistakes and improve when given newer/more information
- 17. Demand prediction Object detection/tracking Motion prediction Route planning Pick-up clustering Voice recognition Supply modeling Occupancy modeling Route planning, ETA, road modeling, low- latency image classifier Elasticity estimation, ETA, route optimization, demand prediction Speech generation, Natural language generations, image classifiers, drop-off clustering
- 18. 2. prototype 3. productionize 1. define 4. measure Launch and Iterate Typical ML Workflow
- 19. UNDERSTAND BUSINESS NEED(S) DEFINE MINIMUM VIABLE PRODUCT (MVP) ○ Customers + cross-functional team ○ Define objectives and key results ○ Data-driven ○ Research ○ Ruthless prioritization 2. prototype 3. productionize 4. measure 1. define Problem Definition
- 20. UNDERSTAND BUSINESS NEED(S) DEFINE MINIMUM VIABLE PRODUCT (MVP) 2. prototype 1. define GET DATA DATA PREPARATION TRAIN MODELS EVALUATE MODELS 3. productionize 4. measure validation computational cost interpretability SQL, Spark data cleansing and pre- processing, R / Python CPU or GPU Exploration
- 21. UNDERSTAND BUSINESS NEED(S) 2. prototype 1. define DATA PREPARATION TRAIN MODELS EVALUATE MODELS 4. measure GET DATA PRODUCTIONIZE MODELS 3. productionize DEPLOY MODELS Engineers + Data Scientists, Java or Go, unit tests MAKE PREDICTIONSReal-time or batch Experimentation and rollout monitoring; Retraining strategy DEFINE MINIMUM VIABLE PRODUCT (MVP) Production
- 22. UNDERSTAND BUSINESS NEED(S) DEFINE MINIMUM VIABLE PRODUCT (MVP) 2. prototype 1. define DATA PREPARATION TRAIN MODELS EVALUATE MODELS GET DATA DEPLOY MODELS PRODUCTIONIZE MODELS MONITOR PREDICTIONS 4. measure MAKE PREDICTIONS 3. productionize Automatically detect degradations GATHER AND ANALYZE INSIGHTS Deep-dive analyses inform future product roadmap Measure
- 23. / Data Science Workbench
- 24. Senior Software Engineer Previously: Big data and big network R&D in gaming, social media & finance Currently: Developer on Data Science Workbench / About Adam
- 25. A growing Data Science community was facing problems with many aspects of their workflows Our world in 2016 NEW Getting Started CollaborationShared Standards Moving Models to Production Scalability Available Features Data Access
- 26. To unleash the productivity of Uber’s Data Science community Mission
- 28. We Wanted More! ● Diverse customers working from same data ○ Data scientists ○ Developers ○ Interns ○ Operations ○ External parties ● Scalability with access to internal data, computation and accounts ● Acceptable licensing cost for large number of casual users
- 29. Introducing Data Science Workbench eng.uber.com/dsw
- 31. Our World Today Getting Started Collaboration Shared Standards Scalability Available Features Data Access Fully hosted 1-click Jupyter Notebook & RStudio IDE Pre-baked Environments Sharing options on notebooks; 1-click Shiny dashboard publication All internal data sources / Multi-DC / Secure / GDPR Compliant Various Session Sizes, Types (CPU, GPU)/Access to Compute Engines Documentation Support
- 32. Common Use-Cases ● Large-scale data exploration ● Feature generation and model training ● Ad-hoc analysis and prototypes ● Review and collaboration
- 34. RStudio and Shiny are trademarks of RStudio, Inc "Jupyter" is a trademark of the NumFOCUS foundation, of which Project Jupyter is a part. "Python" is a registered trademark of the PSF. The Python logos (in several variants) are use trademarks of the PSF as well.
- 35. RStudio and Shiny are trademarks of RStudio, Inc
- 39. The World of Tomorrow! Getting Started Collaboration Customized team environments Social media-like interface; more flexible dashboards Distributed deep learning Low friction workflow Available Features Available Features Moving Models to Production
- 40. DSW Impact Safety Trip classification Risk Driver account check Driver referral risk scoring Uber Eats Restaurant recommendations Support NLP model for support tickets Operations Lifetime value (LTV) model more ! And with that, I will pass you back to Atul to discuss the impact that DSW is having.
- 41. / … one more thing
- 42. We’re hiring! Excited to build the data platform that moves the world? Come join us! http://t.uber.com/datahire San Francisco, Palo Alto, Seattle, Bangalore
- 43. Proprietary and confidential © 2018 Uber Technologies, Inc. All rights reserved. No part of this document may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without permission in writing from Uber. This document is intended only for the use of the individual or entity to whom it is addressed and contains information that is privileged, confidential or otherwise exempt from disclosure under applicable law. All recipients of this document are notified that the information contained herein includes proprietary and confidential information of Uber, and recipient may not make use of, disseminate, or in any way disclose this document or any of the enclosed information to any person other than employees of addressee to the extent necessary for consultations with authorized personnel of Uber. Thank you! and remember, t.uber.com/datahire Questions?