LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar, Head of Engineering, Search, Data Science and Machine Learning, Workday San Francisco

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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.

Technology

Lessons Learnt Building ML Products
LN Renganarayana, Architect ML/Search
Madhura Dudhgaonkar, Head of Engineering ML/Search

Cloud Products to Run Your Business.
Workday Confidential

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

Search and Machine Learning Team
Workday Confidential
San Francisco
Dublin, Ireland
Pleasanton

Pick the validation method thoughtfully!

Plan customer training for novel concepts.

Image courtesy: http://www.businessfundingshow.com/funding/scale-up-companies-will-increase-uks-economic-growth/

Number of
ML models
Data set
size
Number of
ML users

Can’t handcraft an ML
model
Can’t mix data
Built and validated at
scale

Auto ML
Automate model build, validation, serving

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

Invest in right abstractions and tools
to enable domain experts

XO / App Devs,
Service Builders
PaaS
Devs
Data
Scientists &
Engineers
Human in the
Loop
(data analysts)
Product Managers
ML
Platform
PEC & Legal
Platform Thinking

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

Design for use of cloud ML / Platform
Build for scale

● 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

Counter Intuitive Machine Learning for the Industrial Internet of Things: The Industrial Internet of Things (IIoT) is the infrastructure and data flow built around the world’s most valuable things like airplane engines, medical scanners, nuclear power plants, and oil pipelines. These machines and systems require far greater uptime, security, governance, and regulation than the IoT landscape based around consumer activity. In the IIoT the cost of being wrong can be the catastrophic loss of life on a massive scale. Nevertheless, given the growing scale through the digitalization of industrial assets, there is clearly a growing role for machine learning to help augment and automate human decision making. It is against this backdrop that traditional machine learning techniques must be adapted and need based innovations created. We see industrial machine learning as distinct from consumer machine learning and in this talk we will cover the counterintuitive changes of featurization, metrics for model performance, and human-in-the-loop design changes for using machine learning in an industrial environment. Bio: June Andrews is a Principal Data Scientist at Wise.io, From GE Digital working on a machine learning and data science platform for the Industrial Internet of Things, which includes aviation, trains, and power plants. Previously, she worked at Pinterest spearheading the Data Trustworthiness and Signals Program to create a healthy data ecosystem for machine learning. She has also lead efforts at LinkedIn on growth, engagement, and social network analysis to increase economic opportunity for professionals. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell.

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Center of Excellence

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Data driven products

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Redefining Talent Pathway for Digital Age

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Digitalisation has impacted the value of different skills in many industries. The search for digital talent to implement all kinds of enterprises' digital business strategies have been a constant challenge and a delicate balance between cost and value. Since Artificial Intelligence and Automation are certain to play important roles in our workforce, every organisation is looking at how to fully optimise the potential of human and machines working together to unleash new value for the businesses. This talk will cover topics on impact of Digitalisation on Skills, leverage on the Diversity in Digital Talent Pools, Job Redefined To Unleash New Value and redesign Talent Strategy for Digital Age.

When MediaMath was founded in 2007, it established a completely new platform for marketers. In the 8 years since, we've grown to a 700+ person company, but we've had to find new ways to find or create the experts to lead the charge on building and using our platform– experts in a brand new field. At the Glassdoor Rebels of Recruiting Roadshow, Peter Phelan– Chief People Officer at MediaMath– describes how we've kept pace with our relentless need for talent with data-driven recruiting methods and by creating experts where none existed.

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Determining value from machine learning may involve a lot of time and resources--and still not lead you to a business case. Why take on this risk alone? In this session we will show you how to get a head start using machine learning with the Intelligent Business Cloud (IBC) to improve your organization’s processes. An integral part of this is the IBC Machine Learning Workbench, which is invaluable in making it easy to start using machine learning algorithms on process data. On top of that, the Celonis App Store provides tried and tested machine learning apps that can give you accurate predictions at the click of a button. We will show you what you need to do to roll this out and start applying machine learning today! Presenter: Simon Riezebos, Machine Learning Engineer, Celonis

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Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017

MLconf

Deep Reinforcement Learning with Shallow Trees: In this talk, I present Concept Network Reinforcement Learning (CNRL), developed at Bonsai. It is an industrially applicable approach to solving complex tasks using reinforcement learning, which facilitates problem decomposition, allows component reuse, and simplifies reward functions. Inspired by Sutton’s options framework, we introduce the notion of “Concept Networks” which are tree-like structures in which leaves are “sub-concepts” (sub-tasks), representing policies on a subset of state space. The parent (non-leaf) nodes are “Selectors”, containing policies on which sub-concept to choose from the child nodes, at each time during an episode. There will be a high-level overview on the reinforcement learning fundamentals at the beginning of the talk. Bio: Matineh Shaker is an Artificial Intelligence Scientist at Bonsai in Berkeley, CA, where she builds machine learning, reinforcement learning, and deep learning tools and algorithms for general purpose intelligent systems. She was previously a Machine Learning Researcher at Geometric Intelligence, Data Science Fellow at Insight Data Science, Predoctoral Fellow at Harvard Medical School. She received her PhD from Northeastern University with a dissertation in geometry-inspired manifold learning.

Jonas Schneider, Head of Engineering for Robotics, OpenAI

MLconf

Machine Learning Systems at Scale: OpenAI is a non-profit research company, discovering and enacting the path to safe artificial general intelligence. As part of our work, we regularly push the limits of scalability in cutting-edge ML algorithms. We’ve found that in many cases, designing the systems we build around the core algorithms is as important as designing the algorithms themselves. This means that many systems engineering areas, such as distributed computing, networking, and orchestration, are crucial for machine learning to succeed on large problems requiring thousands of computers. As a result, at OpenAI engineers and researchers work closely together to build these large systems as opposed to a strict researcher/engineer split. In this talk, we will go over some of the lessons we’ve learned, and how they come together in the design and internals of our system for learning-based robotics research. Bio: Jonas leads technology development for OpenAI’s robotics group, developing methods to apply machine learning and AI to robots. He also helped build the infrastructure to scale OpenAI’s distributed ML systems to thousands of machines.

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Daniel Shank, Data Scientist, Talla at MLconf SF 2017

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Getting Value Out of Chat Data: Chat-based interfaces are increasingly common, whether as customers interacting with companies or as employees communicating with each other within an organization. Given the large number of chat logs being captured, along with recent advances in natural language processing, there is a desire to leverage this data for both insight generation and machine learning applications. Unfortunately, chat data is user-generated data, meaning it is often noisy and difficult to normalize. It is also mostly short texts and heavily context-dependent, which cause difficulty in applying methods such as topic modeling and information extraction. Despite these challenges, it is still possible to extract useful information from these data sources. In this talk, I will be providing an overview of techniques and practices for working with chat-based user interaction data with a focus on machine-augmented data annotation and unsupervised learning methods. Bio: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.

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Xavier Amatriain, Cofounder & CTO, Curai at MLconf SF 2017

MLconf

ML to Cure the World: The practice of medicine involves diagnosis, treatment, and prevention of diseases. Recent technological breakthroughs have made little dent to the centuries-old system of practicing medicine: complex diagnostic decisions are still mostly dependent on “educated” work-ups of the doctors, and rely on somewhat outdated tools and incomplete data. All of this often leads to imperfect, biased, and, at times, incorrect diagnosis and treatment. With a growing research community as well as tech companies working on AI advances to medicine, the hope for healthcare renaissance is definitely not lost. The emphasis of this talk will be on ML-driven medicine. We will discuss recent AI advancements for aiding medical decision including language understanding, medical knowledge base construction and diagnosis systems. We will discuss the importance of personalized medicine that takes into account not only the user, but also the context, and other metadata. We will also highlight challenges in designing ML-based medical systems that are accurate, but at the same time engaging and trustworthy for the user. Bio: Xavier Amatriain is currently co-founder and CTO of Curai, a stealth startup trying to radically improve healthcare for patients by using AI. Previous to this, he was VP of Engineering at Quora, and Research/engineering Director at Netflix, where he led the team building the famous Netflix recommendation algorithms. Before going into leadership positions in industry, Xavier was a research scientist at Telefonica Research and a research director at UCSB. With over 50 publications (and 3k+ citations) in different fields, Xavier is best known for his work on machine learning in general and recommender systems in particular. He has lectured at different universities both in the US and Spain and is frequently invited as a speaker at conferences and companies.

Dr. Steve Liu, Chief Scientist, Tinder at MLconf SF 2017

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MLconf

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MLconf

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Anima Anadkumar, Principal Scientist, Amazon Web Services, Endowed Professor,...

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Talha Obaid, Email Security, Symantec at MLconf ATL 2017

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A Machine Learning approach for detecting a Malware: The project is to improve the way we detect script based malware using Machine Learning. Malware has become one of the most active channel to deliver threats like Banking Trojans and Ransomware. The talk is aimed at finding a new and effective way to detect the malware. We started with acquiring both malicious and clean samples. Later we performed feature identification, while building on top of existing knowledge base of malware. Then we performed automated feature extraction. After certain feature set is obtained, we teased-out feature which are categorical, interdependent or composite. We applied varying machine learning models, producing both binary and categorical outcomes. We cross validated our results and re-tuned our feature set and our model, until we obtained satisfying results, with least false-positives. We concluded that not all the extracted features are significant, in fact some features are detrimental on the model performance. Once such features are factored-out, it results not only in better match, but also provides a significant gain in performance.

Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

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Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention: PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed. I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.

Jessica Rudd, PhD Student, Analytics and Data Science, Kennesaw State Univers...

MLconf

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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

2. Workday Confidential LN

3. Workday Confidential Madhura

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

9. Early 2014...

10.

11.

12.

13.

14.

15.

16. Educate Stakeholders!

17. k-fold

18.

19.

20. Pick the validation method thoughtfully!

21. Churn

22.

23.

24. 0

25.

26.

27. Nada

28. Why?

29. Interpretability matters!

30. <10

31.

32.

33. Plan customer training for novel concepts.

34. Image courtesy: http://www.businessfundingshow.com/funding/scale-up-companies-will-increase-uks-economic-growth/

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!

46. TM We Are Hiring!

LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar, Head of Engineering, Search, Data Science and Machine Learning, Workday San Francisco

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