This document discusses practical machine learning with prediction APIs. It begins with introductions and an agenda. It then defines machine learning as a type of artificial intelligence that allows computers to learn without explicit programming. Common machine learning algorithms and applications like spam filtering and fraud detection are discussed. The document demonstrates how machine learning works by developing a model, training it with data, testing and analyzing it. It then previews demos of sales and service use cases in Salesforce. The document concludes with a question and answer section.

1. Practical Machine Learning with
Prediction APIs
​ Joseph Ferraro, Salesforce MVP, CTO, Mavens Consulting
​ Vincent Reeder, Technical Architect, Mavens Consulting

2. Joseph Ferraro
Salesforce Developer MVP
CTO, Mavens Consulting
@joeferraro
@mavensmate

3. Vincent Reeder
Technical Architect, Mavens Consulting
@vincentreeder

4. About Mavens Consulting
​ Preferred Healthcare & Life Sciences implementation partner for
salesforce.com and Veeva
​ 50+ Salesforce1 & Heroku consultants located across North America
and Europe
​ 12 Mavens in attendance at #Dreamforce15, speaking in 8 different
technical sessions
​ #mavensDF15

5. Agenda
​ What is machine learning?
​ Practical applications
​ How does it work?
​ Sales Demo with Code
​ Service Demo with Code
​ Summary
​ Q&A

6. Machine Learning
A type of artificial intelligence (AI) that provides computers with the ability to
learn without being explicitly programmed

7. Machine Learning: What is it?
​ Teaching a computer to “do stuff”
​ Automatic
​ Algorithmic
​ Pattern recognition
​ Artificial intelligence
http://www.cs.princeton.edu/courses/archive/spr08/cos511/scribe_notes/
0204.pdf

8. Machine Learning: Practical applications
​ Spam filtering
​ Fraud detection
​ Optical character recognition
​ Medical diagnoses
​ Anticipate customer needs
​ Route requests efficiently
​ Anticipate deals

9. Machine Learning: Common algorithms
​ Decision trees
​ Boosting
• Spam filtering
​ Support vector machines
• Character recognition
http://www.cs.princeton.edu/~schapire/talks/picasso-minicourse.pdf

10. Machine Learning Output
​ Regression: Estimating or predicting a response
• “There is a 92% chance this is spam”
​ Classification: Identify members of a group
• “This text is English”

11. Machine Learning: How does it work?
Develop
Model
Idea
Data
Train
Model
Test/
Analyze
Go live!

12. Machine Learning: How does it work?
​ Starts with an idea, but depends on data
​ Datasets:
• Training – optimal dataset, relationship discovery
• Validation – compare algorithm performance
• Testing – Apply selected algorithm to real-world data
​ Learn!

13. Demo Use Cases
​ Sales Cloud
• Predict when leads will covert to opportunities
​ Serive Cloud
• Gauge customer mood

14. Application
​ Node.js application running on Heroku
​ Exposes endpoints for common machine learning tasks, such as training and prediction
​ Subscribes to Salesforce push topics for Leads and Cases via the streaming API

15. Use Case: Sales

16. Use Case: Support

17. Summary
​ Machine learning teaches computers to do stuff; cool stuff!
​ Collect data, train your model, validate and test it, deploy!
​ Not a perfect science
​ Powerful when used correctly!
​
​ @joeferraro
​ @vincentreeder

18. Q&A

19. Thank you