H2O Machine Learning with Sparkling Water Training

Machine Learning with Sparkling Water

Sparkling Water allows users to combine the fast, scalable machine learning algorithms of H2O with the capabilities of Spark. With Sparkling Water, users can drive computation from Scala/R/Python and utilize the H2O Flow UI, providing an ideal machine learning platform for application developers.

Spark is an elegant and powerful general-purpose, open-source, in-memory platform with tremendous momentum. H2O is an in-memory application for machine learning that is reshaping how people apply math and predictive analytics to their business problems.

Integrating these two open-source environments provides a seamless experience for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark. For any given problem, better interoperability between tools provides a better experience.


H2O Machine Learning with Sparkling Water Training


Course Contents :

1 What is H2O?
2 Sparkling Water Introduction

2.1 Typical Use Cases
2.1.1 Model Building
2.1.2 Data Munging
2.1.3 Stream Processing
2.2 Features
2.3 Supported Data Sources
2.4 Supported Data Formats
2.5 Supported Spark Execution Environments

3 Design

3.1 Data Sharing between Spark and H2O
3.2 Provided Primitives

4 Programming API

4.1 Starting H2O Services
4.2 Memory Allocation
4.3 Converting H2OFrame into RDD[T]
4.4 Converting H2OFrame into DataFrame
4.5 Converting RDD[T] into H2OFrame
4.6 Converting DataFrame into H2OFrame
4.7 Creating H2OFrame from an Existing Key
4.8 Type Map Between H2OFrame and Spark DataFrame Types
4.9 Calling H2O Algorithms
4.10 Using Spark Data Sources with H2OFrame
4.10.1 Reading from H2OFrame
4.10.2 Saving to H2OFrame
4.10.3 Loading and Saving Options
4.10.4 Specifying Saving Mode

5 Deployment

5.1 Referencing Sparkling Water
5.1.1 Using Fatjar
5.1.2 Using Spark Package
5.2 Target Deployment Environments
5.2.1 Local cluster
5.2.2 On Standalone Cluster
5.2.3 On YARN Cluster
5.3 Sparkling Water Configuration Properties

6 Building a Standalone Application

7 What is PySparkling Water?

7.1 Getting Started:
7.2 Using Spark Data Sources
7.2.1 Reading from H2OFrame
7.2.2 Saving to H2OFrame
7.2.3 Loading and Saving Options

8 A Use Case Example

8.1 Predicting Arrival Delay in Minutes – Regression

Machine Learning with Sparkling Water Training Chennai

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Mail: info@bigdatatraining.in
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Weekdays / Fast Track / Weekends / Corporate Training modes available

Machine Learning with Sparkling Water Training  Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati

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