Today, many companies that have lots of data are still struggling to derive value from machine learning (ML) and data science investments. Why? Accessing the data may be difficult. Or maybe it’s poorly labeled. Or vital context is missing. Or there are questions around data integrity. Or standing up an ML service can be cumbersome and complex. At Nuuly, we offer an innovative clothing rental subscription model and are continually evolving our ML solutions to gain insight into the behaviors of our unique customer base as well as provide personalized services. In this session, I’ll share how we used event streaming with Apache Kafka® and Confluent Cloud to address many of the challenges that may be keeping your organization from maximizing the business value of machine learning and data science. First, you’ll see how we ensure that every customer interaction and its business context is collected. Next, I’ll explain how we can replay entire interaction histories using Kafka as a transport layer as well as a persistence layer and a business application processing layer. Order management, inventory management, logistics, subscription management – all of it integrates with Kafka as the common backbone. These data streams enable Nuuly to rapidly prototype and deploy dynamic ML models to support various domains, including pricing, recommendations, product similarity, and warehouse optimization. Join us and learn how Kafka can help improve machine learning and data science initiatives that may not be delivered to their full potential.

1. Maximize the Business Value of
Machine Learning and Data
Science with Kafka
Kafka Summit 2020
Nuuly, Urban Outfitters
Chirag Dadia, Director of Engineering
Tom Szumowski, Sr. Data Scientist
Alan Rosenwinkel, Sr. Data Science Manager

2. Agenda
● Overview of Nuuly
● Customer Experience & Inventory Management
● Warehouse Management
● General Architecture & Kafka
● Data Science at Nuuly
● Q&A

3. What is Nuuly?
A women’s subscription clothing rental service

4. Nuuly 101

5. Nuuly Experience
! 2 3 4
5 6 7 8

6. How does it work?
Kafka as the central nervous system

7. Kafka’s Role at Nuuly
Kafka stream processors orchestrate all the business workflows

8. Data Science at Nuuly

9. How does Kafka help the Data Science (DS) workflow?
Extraction: Flexibly connect generic or custom consumers to hydrate data stores
Access: Safe access to any data stream without new apps
Kafka helps Data Scientists access production data for offline analysis

10. How does Kafka help the Data Science (DS) workflow?
Integration: Low-latency streaming data for real-time apps
Deployment: Push artifacts into production apps
Kafka helps bridge the gap between DS artifacts and operational data feeds

11. How do these workflows tie into the Nuuly business?
Online
Optimization
Order
Fulfillment
Personalization
● Recommendations
● Finding similar products
● Optimizing garment pick-
paths in warehouse
● Enabling data-flows for
real-time user feedback
assessment
Offline Processing Batch Topic Processing
Real-Time Feedback
Streams

12. Personalization

13. Personalization: Recommendations
Objective: Simplify user’s box-selection journey through
recommendations
Data Science Solution: Custom Collaborative Filtering
Business Value:
● Promote purchases
● Retain customers
Technical Challenges:
● Resources: GPU-heavy on train, memory-heavy on serve
● Scope: Used across various site components, with
varying configurations
● Scale: several thousands users and products
● Latency: Response time within 100 ms

14. Integrating Personalization with Kafka
1. Real-time event
streams that land in a
queryable database
2. User recommendations
computed offline 3. Resulting artifact feeds a real-
time Kafka State Store, used for
trays, browse pages, etc.
User/Item
Interactions
Offline
Nightly
Low-latency
rankings
fetch
Rankings +
Context Metadata
Offline workflow that fuels a low-latency, real-time Kafka State Store

15. Order Fulfillment

16. Order Fulfillment: Technical Challenges
Objective: Minimize walking path in warehouse
Business Value: Reduce operational costs, meet shipping SLAs
Data Science Solution: Route Planning algorithms
Technical Challenges:
● Resources: Very heavy CPU/memory required online
● Reliability: Interacting with critical data paths
● Timeliness: Must always have batches of N orders ready to
disperse
● Optimality: Works best with lots of orders (not streaming)
How do we safely connect in our DS app while ensuring consistency in orders?

17. Connecting into Core Business Flow with Kafka
Periodic cronjob that processes all (non-tombstoned)
messages from first offset in batch

18. Online Experimentation

19. Online Optimization
Objective:
● Adaptively update backend algorithms based
on renting and purchasing feedback
Business Value:
● Promotion optimization
● Inventory/assortment optimization
Other Operational Benefits:
● Reduce experimentation tracking overhead
● Reduce incremental deployment updates
Data Science Solution
● Online Contextual Multi-Armed Bandits
Technical Challenges:
● Delayed feedback, up to a month after rented
● Multiple data sources to capture {state, action,
reward}
Source: https://slivkins.com/work/bandits-svc
Product Value Promotion Rented, Purchased, Rated, etc.
weeks later ...

20. Developing Closed-Loop Feedback with Kafka
1. Derive “context”
(feature extraction)
2. Select action,
triggered by event
3. Optimize:
Incrementally
update model
Leveraging real-time producers &
consumers to enable closed-loop
feedback

21. Q & A