Advanced Spark Meetup - Jan 12, 2016
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Training & Serving NLP/Spark ML Models in a Distributed Cloud-based Infrastructure
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Advanced Spark Meetup - Jan 12, 2016
- 1. Michelle Casbon January 12, 2016 – Advanced Apache Spark Meetup Training & Serving NLP Models in a Distributed Cloud- based Infrastructure
- 2. 2 What do we do? • Idibon creates adaptive machine intelligence that can analyze text in any language natural language text social media structured insights
- 3. 3 • Background • Platform description • Why we chose Spark • How we’re using Spark ML & MLlib • Challenges of adopting Spark in a distributed NLP system Agenda
- 4. 4 What are our use cases? Intent to purchase Global health trends Interactive Voice Response Multilingual news SMS Prioritization Supply Chain Risk Change reception
- 5. How do we do it?
- 6. • Fewer annotations • Lower costs • Less time spent training • Higher accuracy • Improves over time labeled training set human annotation intelligent queuing & machine learning unlabeled pool Adaptive learning
- 7. 7 How do we do it? Dataset Models Identification2 Cleansing3 Training data creation4 Quality Control5 Creation6 Hyperparameter Tuning 7 Intelligent Queueing 8 Rule Creation9 10 Unseen Data Prediction Goal Definition1
- 8. What does our platform look like?
- 9. 9 • Wide variety of algorithms • Active development • Open source • Industry-standard algorithm implementation • Intended for use in enterprise applications • Scalability Why are we using Spark?
- 10. 10 • 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
- 11. 11 Feature Extraction Extract Content Tokenize Bigrams Trigrams Feature Lookup [1.0, 0.0, 3.0] Vector
- 12. 12 Training LogisticRegression WithLBFGS [1.0, [1.0, 0.0, 3.0]] LabeledPoint Model Storage [1.0, 0.0, 3.0] Vector Add classification LogisticRegressionModel
- 13. 13 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
- 14. 14 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 ...
- 15. 15 How do we fit Spark into our existing system? Core functionality Idibon custom ML … REST API ML persistence layer
- 16. 16 • 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?
- 17. 17 • Analyzing human language is hard • We’re using the most exciting parts of Spark to build performant NLP systems that are faster & better than ever before Summary
Notas do Editor
- Detecting intent-to-buy in twitter data Extracting insights about global health trends Making the call center iVR smarter Informing investors of company performance based on news sentiment Identifying supply chain risk in news articles Understanding user reception of code pushes in online games Prioritizing urgent SMS messages for UNICEF
- API: Document uploads, model training, cross-validation, annotation aggregation, queueing, topic modeling, prediction, IAA
- Persistence layer Docker, Lambda, offline, on-device 0 Input/output streams vs. flat files slf4j