Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library. Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs – Model Object Optimized – a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs. We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.

1. Jakub Háva, Michal Malohlava; H2O.ai
Productionizing H2O
Models with Apache
Spark
#ML4SAIS

2. #ML4SAIS
Who are we?
• Michal
• Chief Architect of Platforms at H2O.ai
• Creator of Sparkling Water
• Ph.D at Charles University (CZ), PostDoc at Purdue Uni (US)
• Kuba
• Senior Software engineer at H2O.ai - Core Sparkling Water
• Master’s at Charles University (CZ)
• Implemented high-performance cluster monitoring tool for
JVM based languages (JNI, JVMTI, instrumentation)
2

3. Machine Learning
(ML) Lifecycle
#ML4SAIS

4. 4
Model
Training
Algorithm
Feature
Engineering
Model Pipeline Building
Training
Predictions
Data Engineering
Basic ML Lifecycle
#ML4SAIS

5. 5
Model
Training
Algorithm
Feature
Engineering
Featurization Pipeline
Model
Model Pipeline Building
Training
Predictions
Deployment
Predictions
Data Engineering
Model Pipeline Deployment
Basic ML Lifecycle
#ML4SAIS

6. Example Implementations
6#ML4SAIS
Data Engineering
Feature
Engineering
Training Algorithm
Deployment
Pipeline
Model
Spark H2O Spark H2O MOJO
Spark H2O Driverless AI Spark
H2O Driverless AI
MOJO
Model Building Model Deployment

7. H2O + Spark =
Sparkling
Water
#ML4SAIS

8. #ML4SAIS
H2O + Spark
• H2O
• Machine Learning Library
• Distributed Algorithms
• For ML experts
• Sparkling Water
• Integrates H2O & Spark Ecosystems
• Transparent for Spark users
• Based on Spark pipelines & H2O
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9. Basic ML Lifecycle: Sparkling Water
9
Model
Training
Algorithm
Feature
Engineering
Spark Transformers H2O MOJO Model
Training
Predictions
Deployment
Predictions
AutoML
Pipeline
#ML4SAIS

10. Demo:
Spark Pipeline
#ML4SAIS

11. H2O Driverless AI
#ML4SAIS

12. #ML4SAIS
H2O Driverless AI
• What if I’m not expert ?
• H2O Driverless AI
• H2O Driverless AI
• No expert knowledge required
• Automatic Feature Engineering & ML
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13. Basic ML Lifecycle: Driverless AI
13
Model
Training
Algorithm
Feature
Engineering
Driverless AI Feature Transformations Driverless AI Model
Training
Predictions
Deployment
Predictions
PipelineDriverless AI MOJO as
#ML4SAIS

14. Demo:
Driverless AI as
Spark Pipeline
#ML4SAIS

15. 15#ML4SAIS

16. Driverless AI Pipeline
16#ML4SAIS

17. Governed ML
Lifecycle
#ML4SAIS

18. Governed ML Lifecycle
18
Model
Training
Algorithm
Feature
Engineering
Featurization Pipeline
Model
Model Pipeline Building
Training
Predictions
Deployment
Predictions
Model
Management
Data Engineering
Model Pipeline Deployment
Model
Monitoring
Auto
Documentation
#ML4SAIS

19. #ML4SAIS
Materials
19
https://bit.ly/2sxowxD

20. #ML4SAIS
Sparkling Water
enables
deployment of
H2O ML models
with Spark
Pipelines
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Thank you!