Jakub Hava, H2O.ai - Productionizing Apache Spark Models using H2O - H2O World SF

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This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/PavE9px22Mo 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. Bio: Jakub (or “Kuba” as we call him) completed his Bachelor’s Degree in Computer Science and Master’s Degree in Software Systems at Charles University in Prague. As a bachelor’s thesis, Kuba wrote a small platform for distributed computing of any types of tasks. During his master’s degree studies, he developed a cluster monitoring tool for JVM based languages which makes debugging and reasoning the performance of distributed systems easier using a concept called distributed stack traces. Kuba enjoys dealing with problems and learning new programming languages. At H2O.ai, Kuba works on Sparkling Water. Aside from programming, Kuba enjoys exploring new cultures and bouldering. He’s also a big fan of tea preparation and the associated ceremony.

Technology

Productionizing
H2O Models
using Apache
Spark
Jakub Hava
Senior Software Engineer
H2O.ai
https://www.linkedin.com/in/havaj/
#H2OWORLD

Who Are We?
• 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)
• Michal
• VP of Engineering at H2O.ai
• Creator of Sparkling Water
• Ph.D at Charles University (CZ), PostDoc at Purdue (US)

Basic ML Lifecycle
Model
Training
Algorithm
Feature
Engineering
Model Pipeline Building
Training
Predictions
Data Engineering

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

Example Implementations
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

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

Basic ML Lifecycle: Sparkling Water
Model
Training
Algorithm
Feature
Engineering
Spark Transformers
H2O MOJO Model
Training
Predictions
Deployment
Predictions
AutoML
Pipeline

H2O Driverless AI
• What if I’m not expert ?
• H2O Driverless AI
• H2O Driverless AI
• No expert knowledge required
• Automatic Feature Engineering & ML

Basic ML Lifecycle: Driverless AI
Model
Training
Algorithm
Feature
Engineering
Driverless AI Feature Transformations
Driverless AI Model
Training
Predictions
Deployment
Predictions
PipelineDriverless AI MOJO as

Governed ML Lifecycle
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

Thank you!
Sparkling
Water enables
deployment of
H2O ML
models with
Spark
Pipelines

This meetup was recorded in Mountain View, CA on January 10th, 2019. Video recording from the meetup can be viewed here: https://youtu.be/yN26i7e_BtM 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. Speaker's Bio: Michal is the VP of Engineering at H2O.ai! Michal is a geek, developer, Java, Linux, programming languages enthusiast developing software for over 15 years. He obtained PhD from the Charles University in Prague in 2012 and post-doc at Purdue University. During his studies he was interested in construction of not only distributed but also embedded and real-time component-based systems using model-driven methods and domain-specific languages. He participated in design and development of various systems including SOFA and Fractal component systems or jPapabench control system.

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Databricks

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.

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Jakub Hava, H2O.ai - Productionizing Apache Spark Models using H2O - H2O World SF

1. Productionizing H2O Models using Apache Spark Jakub Hava Senior Software Engineer H2O.ai https://www.linkedin.com/in/havaj/ #H2OWORLD

2. Who Are We? • 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) • Michal • VP of Engineering at H2O.ai • Creator of Sparkling Water • Ph.D at Charles University (CZ), PostDoc at Purdue (US)

3. Machine Learning (ML) Lifecycle

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

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

6. Example Implementations 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

8. 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

9. Basic ML Lifecycle: Sparkling Water Model Training Algorithm Feature Engineering Spark Transformers H2O MOJO Model Training Predictions Deployment Predictions AutoML Pipeline

10. Demo: Spark Pipeline

11. H2O Driverless AI

12. H2O Driverless AI • What if I’m not expert ? • H2O Driverless AI • H2O Driverless AI • No expert knowledge required • Automatic Feature Engineering & ML

13. Basic ML Lifecycle: Driverless AI Model Training Algorithm Feature Engineering Driverless AI Feature Transformations Driverless AI Model Training Predictions Deployment Predictions PipelineDriverless AI MOJO as

14. Demo: Driverless AI as Spark Pipeline

15. !15

16. Driverless AI Pipeline

17. Governed ML Lifecycle

18. Governed ML Lifecycle 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

19. Materials https://bit.ly/2RCStWM

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

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