This document discusses building an ideal machine learning stack for real-time applications. It provides an overview of machine learning and real-time use cases. It also demonstrates an example machine learning pipeline for fraud detection using labeled historical data to train a model and score new data in real-time. Finally, it discusses industry examples of using machine learning models for advertising optimization, anomaly detection, and image recognition and provides a demo of predicting images from the MNIST data set.

1. Building the Ideal Stack
for Machine Learning

2. 2
Agenda
o Machine Learning Overview
o The Need for Real-Time
o Real-Time Machine Learning
Use Cases
o Demo

3. MISSION
Make every company a real-time enterprise
PRODUCT
Database Platform: Real-Time | Scalable | Proven
Ranked #1 Operational Data Warehouse
ABOUT
Founders are former Facebook, SQL Server database engineers
YC alumni with $85m in funding from top tier investors
MemSQL at a Glance

4. MACHINE LEARNING
Running a mathematical
model on a data set, where
the model is trained from
data using some
optimization technique
DATA SCIENCE
Broader field of training,
running, testing, and
productizing data models

5. 5
Example Types of Machine Learning
SUPERVISED LEARNING
Regression
Classification
UNSUPERVISED LEARNING
Cluster Analysis

6. 6
Example Machine Learning Pipeline
NEW DATA
FEATURES
PREDICTION

7. 7
Example Machine Learning Pipeline
NEW DATA
FEATURES
PREDICTION
Feature
Extraction
Model
Scoring

8. 8
Example Machine Learning Pipeline
(Mathematical function)
(Arbitrary computation)
Feature
Extraction
Model
Scoring
NEW DATA
FEATURES
PREDICTION

9. 9
Example Machine Learning Pipeline
Model Accuracy
Feature
Extraction
Model
Scoring
NEW DATA
FEATURES
PREDICTION
LABELED
HISTORICAL DATA
FEATURES
PREDICTION
LABELS
Validation

10. 10
Example Machine Learning Pipeline: Fraud Detection
Model Accuracy
Feature
Extraction
Model
Scoring
NEW DATA
FEATURES
PREDICTION
LABELED
HISTORICAL DATA
FEATURES
PREDICTION
LABELS
Validation
Previously labeled
credit card
transactions
X% of predictions are
accurately predicted to
be fraud
New credit card
transactions
Yes/No fraud
prediction
BI DASHBOARD

11. 11
Building your Machine Learning Stack for Experimentation
Model Accuracy
LABELED
HISTORICAL DATA
FEATURES
PREDICTION
LABELS
PROGRAMMING STACK
Python/NumPy Stack
R
Others: Matlab, Julia
VISUALIZATION TOOL
Interactive
Customizable
Sharable

12. 12
Building your Machine Learning Stack for Productization
NEW DATA
FEATURES
PREDICTION
o Performance
o Message Delivery Semantics
o Message Queues
o Distributed Computation
o Persistent Database
o Business Intelligence Tool

13. Going Real-Time is the Next Phase for Big Data
MORE
SENSORS
MORE
INTERCONNECTIVITY
MORE
USER DEMAND

14. 14
Real-Time Machine Learning
Model Accuracy
Feature
Extraction
Model
Scoring
NEW DATA
FEATURES
PREDICTION
LABELED
HISTORICAL DATA
FEATURES
PREDICTION
LABELS
Validation

15. 15
Real-Time Machine Learning
Model Accuracy
Feature
Extraction
Model
Scoring
NEW DATA
FEATURES
PREDICTION
LABELED
HISTORICAL DATA
FEATURES
PREDICTION
LABELS
Validation

16. Machine Learning
Industry Examples

17. New O’REILLY BOOK:
The Path to Predictive Analytics
and Machine Learning
memsql.com/oreillyml

18. Real-time
website
traffic data
Spark MLlib Predictive Model
Advertising Optimization in AdTech Industry
Determine the best ads to serve to users
based on previous behavior and market
segmentation
Machine learning model implemented using
Spark MLlib
BUSINESS
LOGIC
18

19. Real-Time
Anomaly Alerts
...
Sensor Activity
Anomaly Detection in Energy Industry
Proactively detect non-optimally
functioning oil drills based on sensor
activity, and correct issues before damage is
done
Machine learning model is SAS model
exported in PMML format run inside Spark
PMML Predictive Model

20. Facial image
content
Machine
Learning
Model
Image recognition
dashboard
Image Detection in Social Media Industry
Find matches for faces found in millions of
image files, and determine if the face
matches a set of known “friends”
Machine learning model is a vector dot
product calculation between image feature
vectors, all done in MemSQL

21. MNIST
data
MemSQL
Pipeline SQL
Leverage the TensorFlow machine
learning library to predict images in the
MNIST training data set, and store
predictions in MemSQL
MNIST Data Set Demo

22. DEMO

23. Q&A

24. Thank You!
Try MemSQL: memsql.com/download
Contact: info@memsql.com
Twitter: @memsql