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Zipline is Airbnb’s data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Zipline reduces this task from months to days – by making the process declarative. It allows data scientists to easily define features in a simple configuration language. The framework then provides access to point-in-time correct features – for both – offline model training and online inference. In this talk we will describe the architecture of our system and the algorithm that makes the problem of efficient point-in-time correct feature generation, tractable.
The attendee will learn
Importance of point-in-time correct features for achieving better ML model performance
Importance of using change data capture for generating feature views
An algorithm – to efficiently generate features over change data. We use interval trees to efficiently compress time series features. The algorithm allows generating feature aggregates over this compressed representation.
A lambda architecture – that enables using the above algorithm – for online feature generation.
A framework, based on category theory, to understand how feature aggregations be distributed, and independently composed.
While the talk if fairly technical – we will introduce all the concepts from first principles with examples. Basic understanding of data-parallel distributed computation and machine learning might help, but are not required.
1 . Evgeny Shapiro, Varant Zanoyan / Oct 2019 / Airbnb Zipline: Declarative Feature Engineering
2 . 1. The machine learning workﬂow 2. The feature engineering problem 3. Zipline as a solution 4. Implementation Agenda 5. Results 6. Q&A
3 . THE MACHINE LEARNING WORKFLOW IN PRODUCTION
4 . ● Goal: Make a prediction about the world given incomplete data ● Labels: Prediction Target ● Features: known information to learn from Machine Learning ● Training output: model weights/parameters ● Serving: online feature ● Assumption: Training and serving distribution is the same (consistency)
5 . ● Goal: Make a prediction about the world given incomplete data ● Labels: Prediction Target ● Features: known information to learn from Machine Learning ● Training output: model weights/parameters ● Serving: online feature ● Assumption: Training and serving distribution is the same (consistency)
6 . ML applications Unstructured Structured # of data sources Image Object Chat apps Credit scores Customer LTV Fraud Ads classification detection Personalized search NLP ● Most of the data is available at once: full ● Data arrives steadily as user interacts with the image platform ● Features are automatically extracted from few ● Features extracted from many event streams: (often one) data stream: ○ logins ○ words from a text ○ clicks ○ pixels from an image ○ bookings ○ page views, etc ● Iterative manual feature engineering
7 . Feature Engineering Unstructured Structured # of data sources Image Object Chat apps Credit scores Customer LTV Fraud Ads classification detection Personalized search NLP N-grams from a text Sum of past purchases in last 7 days
8 . ● Oﬄine Batch (email marketing) ○ Does not require serving feature in production ○ Online/Oﬄine consistency is not a problem Oﬄine Batch vs Online Real-time ● Online Real-time (personalized search) ○ Does require serving feature in production ○ Online/Oﬄine consistency is a problem
9 . Feature engineering For the structured online use case
10 . “We recognize that a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code” – Sculley, NIPS 2015
11 . ML Models Feature values Training data set User behavior & business processes F1 0 5 7 4 7 F2 3 2 4 3 Product Pred P1 Problem Label L L Time
12 . Log-based training Online Offline (Hive) Service Scoring log Scoring Labels DB Application (daily) Service Keys, features, Training Set score KV Event Bus
13 . ● Easy to implement ● Any production-available data point can be used Log-based training for training and scoring is great † ● Log can be used for audit and debug purposes ● Consistency is guaranteed † May capture accidental data distribution shifts, requires upfront implementation of new features in production, may slow down feature iteration cycle, prevents feature sharing between models, increases product experimentation cycle, severely limits your ability to react to incidents, fixing production issues might degrade model performance, may decrease sleep time during on-call rotations. Consult with your architect before taking log-based training approach.
14 . ● Sharing features is hard ● Testing new features requires production implementation The Fine Print up close ● May capture accidental data shifts (bugs, downed services) ● Slows down the iteration cycle ● Limits agility in reacting to production incidents
15 . Slowdown of experimentation Feature values Training data set User behavior & business processes F1 0 5 7 4 7 4 F2 3 2 4 3 2 F3 ? 4 ? 8 Product Pred P1 P2 Problem Label L L L Time
16 . ● Some models are time-dependent (seasonality) ● For some problems label maturity is on the order of months Why is that a problem? ● Production incidents lead to dirty data in training ● Labels are scarce and expensive to acquire → Months-long iteration cycles → Hard to maintain models in production → Cannot address shifts in data quickly
17 . ● Backﬁll features ○ Quick! ● Single feature deﬁnition for production and What do we want? training ● Automatic pipelines for training and scoring
19 . Zipline: feature management system Fast Backfills - Data Warehouse Training Model Pipeline Training Set Feature Definition Consistency Serving Online Scoring Pipeline Vector Low Latency Serving - Online Environment
20 .Feature deﬁnition
21 .Training Set API The time at which we made the prediction, also the time at which we would log the feature
22 .Training Set
23 . If you missed it... Training set = f(features, keys, timestamps)
25 . ● Complex features: ○ Only worth it if the gain is huge ○ Require complex computations ○ Harder to interpret ○ Harder to maintain Feature philosophy ● Simple features: ○ Easier to maintain ○ Faster to compute ○ Cumulatively provide huge gain for the model
26 . ● Sum, Count ● Min, Max ● First, Last Supported ● Last N operations ● Statistical moments ● Approx unique count ● Approx percentile ● Bloom ﬁlters + time windows for all operations!
27 . ● Commutative: a ⊕ b = b ⊕ a ● Associative: (a ⊕ b) ⊕ c = a ⊕ (b ⊕ c) Operation ● Additional optimizations: requirements ○ Reversible: a ⊕ ? = c ● Must be O(1) in compute ⇒ must be O(1) in space
28 . Serving pipeline: lambda Batch KV Feature Zipline Client Definition Streaming KV
29 . Data skew: large number of events Page views user ts 1 2019-10-01 00:00:01 1 2019-10-01 00:00:02 50% ... ... 1 2019-10-01 23:59:59 2 2019-10-02 15:20:30 3 2019-10-12 16:11:44 Use aggregateByKey to ensure data is locally combined on the first stage before sent final merge