Machine Learning in the Cloud

 Mark Masterson  Application Engineer  mark.masterson@kenandy.com  @MarkMastersonSF  

Building a better Forecast with H2O and Salesforce

Mark Masterson Application Engineer, Kenandy Inc.

 Basics of Machine Learning

 H2O and how it can be used to perform Machine Learning tasks

 How Salesforce can be enhanced by integrating with Machine Learning tools

Agenda

Basics of Machine Learning

 Machine Learning consists of building predictive models that are learned from data.

 Supervised Learning: given data x, predict y

•  Two major uses: classification and regression

  Unsupervised Learning: What are interesting things about data x

Basics of Machine Learning

Classification

Regression

 H2O - http://h2o.ai/

 Amazon Machine Learning - https://aws.amazon.com/machine-learning/

 Microsoft Azure Machine Learning - http://azure.microsoft.com/en-us/services/machine-learning/

 Google Prediction API - https://cloud.google.com/prediction/docs

 Apache Spark - http://spark.apache.org/

Machine Learning Offerings

H2O

 Open Source predictive analytics platform

 Easy to use Web Interface

 NanoFast™ Scoring Engine

 Robust REST API Support

 Distributed and In-Memory

 Support for Java, Python, R, and Scala

Why H2O?

 Deep Learning

 Distributed Random Forest

 Gradient Boosting Machine (GBM)

 Generalized Linear Model (GLM)

 K-Means

 Native Bayes

H2O Algorithms

Integration with Salesforce

Sales Collaborative Forecast

 Advantages

•  Gives sales users a quick view into the current status, and values of opportunities

•  Excellent User Interface for Sales Managers and Sales People to collaborate on sales goals

Disadvantages

•  Not a true “forecast” informed by statistical analysis

•  Relies on human input and analysis, which is prone to error.

Forecast Advantages and Disadvantages

 Data supplemented with prediction models provided by H2O

 Integration with H2O gives us access to highly performant Machine Learning

Solution: Forecasting 2.0

 Resources

•  H2O – http://h2o.ai/product/

•  H2O Github – https://github.com/h2oai/h2o-3/

Questions and Answers Q&A

Thank you