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Michelle Casbon
January 16, 2016 – Data Day Texas, Austin
Under the Hood of Idibon’s Scalable NLP Services
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• Idibon creates adaptive machine intelligence that can analyze text in any language
What do we do?
natural language text
social media
structured insights
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• Background• Process walk-through• Platform description• Why we chose Spark• How we’re using Spark ML & MLlib• Challenges of adopting Spark in a distributed NLP
system
Agenda
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Supply Chain RiskIntent to purchase
What are our use cases?
Global health trends
Interactive Voice Response
Multilingual news SMS PrioritizationChange reception
How do we do it?
• Fewer annotations• Lower costs• Less time spent training• Higher accuracy• Improves over time
labeled training set
human annotation intelligent queuing&
machine learning
unlabeled poolAdaptive learning
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How do we do it?Dataset
Models
Identification2
Cleansing3
Training data creation4
Quality Control5
Creation6
Hyperparameter Tuning7
Intelligent Queueing
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Rule Creation910 Unseen Data
Prediction
Goal Definition1
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• Real-time API support• Document storage• 1000’s of individual predictions per second• Continuous training• Hyperparameter optimization
Scalability Challenges
What does our platform look like?
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• Wide variety of algorithms• Active development• Open source• Industry-standard algorithm implementation• Intended for use in enterprise applications• Scalability
Why are we using Spark?
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• Feature Extraction• TF-IDF• Word2Vec• Dimensionality reduction
• Training• Logistic Regression• SVM• Naïve Bayes• LDA
• Prediction• Evaluation metrics
How are we using Spark?
[1.0, [1.0, 0.0, 3.0]]
Feature Extraction
Training
Prediction
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Feature Extraction
Extract Content Tokenize
Bigrams
Trigrams
Feature Lookup
[1.0, 0.0, 3.0]
Vector
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Training
LogisticRegressionWithLBFGS
[1.0, [1.0, 0.0, 3.0]]
LabeledPoint
Model Storage
[1.0, 0.0, 3.0]
Vector
Add classification
LogisticRegressionModel
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Prediction
Extract Content Tokenize
Bigrams
Trigrams
Feature Lookup
[0.0, 1.0, 4.0]
Vector
Model Lookup
Predict
New tweet
[0.0, 1.0, 4.0]
Vector
Classification Lookup
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How do we provide online predictions with Spark?
… if you have small data
Task Time in µs
Vector prediction 300
DataFrame prediction 7800
DataFrames are slow ...
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How do we fit Spark into our existing system?
Core functionality
Idiboncustom ML
…
REST API
ML persistence layer
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• Real-time operationalization of many, many models• Embed within different platforms• Single save/load framework• Rapidly incorporate new NLP features• Logging/monitoring standardized & abstracted
How does a persistence layer enable us to use Spark?
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• Analyzing human language is hard• We’re using exciting tools to build performant NLP
systems that are faster & better than ever before• Introduce yourself!
Summary
