LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar, Head of Engineering, Search, Data Science and Machine Learning, Workday San Francisco
Lessons Learnt from building ML Products for enterprise SaaS: Having spent the last 4+ years productizing ML powered enterprise products, we have learnt a lot! Join us to hear the stories of our stumbles (ahem learnings) in applying machine learning to solve business problems for Fortune 500 companies. Our hands-on experience has shaped our product strategy, ML platform design and organization’s operational principles. And the investments we made based on our learnings have helped us drastically improve our time to market for ML products. Come on by to hear the technical and organizational challenges (and some solutions) in building ML products for enterprise SaaS. Hopefully our learnings will be useful in your journey. Bio: LN leads the architecture and design of Workday’s ML Platform and Services. He is all about building large scale distributed systems and data platforms. Currently his days (and some nights) are spent on solving the challenges in building ML products for Enterprise SaaS. LN’s career spans across HP, IBM Research, Symantec and now Workday. At Symantec, he was the architect and lead of a streaming platform that ingested and processed 2+ billions of events per day. As a Research Staff Member at IBM T.J. Watson Research Center, LN built optimizations for automatic parallelization, techniques for approximate computing, deployment automation for OpenStack, and analytics for large scale cloud services. LN holds a Ph.D. in Computer Science from Colorado State University and has published more than 40 technical publications / patents. His work has received awards from ACM, IBM, and HP. Bio:Madhura Dudhgaonkar is responsible for leading Workday’s search, data science and machine learning teams based in San Francisco. Her teams have spent ~4 years building machine learning products used by Fortune 500 companies. Her experience ranges from being a hands-on engineer to leading large engineering organizations. Madhura’s career spans across SUN Microsystems, Adobe and now Workday. During her career, she has been involved with building a variety of products – from developing Java Language to building a version 1.0 consumer product to building enterprise SaaS products. She holds a bachelor’s degree in electronics and telecommunications and a master’s degree in math and computer science. Madhura is originally from a small town in India and came to the United States to pursue her passion in technology. She currently calls San Francisco home, and despite nine years here, can’t get enough of its hilly charm, the diversity of people, culture, and experiences.
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LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar, Head of Engineering, Search, Data Science and Machine Learning, Workday San Francisco
- 1. Lessons Learnt Building ML Products LN Renganarayana, Architect ML/Search Madhura Dudhgaonkar, Head of Engineering ML/Search
- 6. Cloud Products to Run Your Business. Workday Confidential
- 7. Pace of Innovation 2007-2011 1 Financial Management Human Capital Management Payroll U.S. Expenses & Procurement Mobile Applications 2012 Talent Education and Government Time Tracking 2014 - 2017 Big Data Recruiting Insights Applications Payroll U.K. Inventory Payroll France Learning Planning Retention Risk Timeseries Forecast Expense - OCR Search Relevance Skills GraphLearning Reco Prism Analytics
- 8. Search and Machine Learning Team Workday Confidential San Francisco Dublin, Ireland Pleasanton
- 17. k-fold
- 20. Pick the validation method thoughtfully!
- 21. Churn
- 24. 0
- 27. Nada
- 28. Why?
- 30. <10
- 33. Plan customer training for novel concepts.
- 35. Number of ML models Data set size Number of ML users
- 36. Understand (new) dimensions of scale
- 37. Can’t handcraft an ML model Can’t mix data Built and validated at scale
- 38. Auto ML Automate model build, validation, serving
- 39. Expenses Payroll Planning Learning Talent Time TrackingProcurement Recruiting InventoryProjectsFinancials StudentHR Enable exploration and fast prototyping of ML Apps Frameworks / APIs / UX for building ML-powered Apps
- 40. Invest in right abstractions and tools to enable domain experts
- 41. XO / App Devs, Service Builders PaaS Devs Data Scientists & Engineers Human in the Loop (data analysts) Product Managers ML Platform PEC & Legal Platform Thinking
- 42. Workday ML Platform HDFS KV Store SPARK JVMCloud ML Build Platforms (AWS) MySQL Deep Learning (MxNet) Elasticsearch Cloud ML Services (models) Scheduler & Orchestrator Model Builder Model Manager User Action Collector Data Transport Time series Forecasting Anomaly Detection OCR RecoNLPKnowledge Graph Query Intent Stanford NLP API ( XO, REST, MsgQ) Retention Risk LearningExpense BI Workday Cloud Platform Planning Recruiting ML powered Apps ML Services Foundation Services Compute & Persistence
- 43. Design for use of cloud ML / Platform Build for scale
- 44. ● Educate Stakeholders ● Result interpretability matters ● Thoughtfully pick the validation method ● Plan for end user training ● Understand what scale means for you ● Auto ML (as much as possible) ● Build tools to enable domain experts ● Build for Scale and use of Cloud ML
- 45. Thank You! We are Hiring!