Self-Service Data Science for Leveraging ML & AI on All of Your Data

© 2017 MapR TechnologiesMapR Confidential 1
Self-Service Data Science for
Leveraging ML & AI on All of Your
Data:
Introducing the MapR Data Science Refinery
Rachel Silver
Product Manager – Data Science & Analytics
11/16/17

Summary
• Why Companies Invest In ML/AI
• Winning With a Data First Approach
• Introducing the MapR Data Science Refinery
• Deep Dive & Demos
– Ease of Deployment
– Data Exploration
– Extensibility & Collaboration

Why Companies Invest In ML/AI

Where AI Creates Value In The Value Chain
Produce
Optimized Production &
Maintenance
Provide rich, personal, and convenient
user experiences.
Project
Smarter R&D and
forecasting
Promote
Targeted Sales &
Marketing
Source: McKinsey Global Institute – Artificial Intelligence / The Next Digital Frontier? (2017)

Project Where The Next Threat Will Come From
Deep security analytics and advanced persistent threat (APT) detection
• Centralization and
visibility of all data
from an information
security perspective
• Reduced risk of
data breaches from
DDOS and APT
attacks
• Real-time insights
into what is
happening within
the environment
OBJECTIVE
• Early detection of data breaches and suspicious activity
• Aggregate and retain all security related data into a single central store and
then build statistical models to detect abnormal activity within the
environment.
• Get insights into what are insiders doing within the environment
CHALLENGES
• Existing SIEM solution could not scale
• Current solutions do not work well for “unknown” threats
SOLUTION
• Leverage MapR-DB for fast data ingestion and query performance
• MapR provided the deep storage and machine learning algorithms
• NFS enabled easy integration with the IT ecosystem
Retail
Bank

Source
1
Source
2
Source
1000
Houston
MAPR
Core
Cluster
Time to insight (48 hrs)
Manual Process
Before Edge
Source
1
Source
2
Source
1000
Houston
MAPR
Core
Cluster
Time to insight (<2 hrs)
Automated Process
1000s of
Oil & Drill Sources
Will do Pre Processing locally +at Core
(Custom App + Down Sampling)
After Edge
Produce More Efficiently
ML aggregation and processing at the edge optimizes production
Oil & Gas
company

Promote personalized offers in real-time
Targeting credit card customers using Recommendation Engine
A Global Financial
Services company
wanted to offer real-time
localized & personalized
recommendations to
their credit card holders
using ML/AI
OBJECTIVE
• Increase revenue and customer loyalty through real-time personalized offers
generated by a recommendation engine
CHALLENGES
• In order to be accurate, data had to be updated on a real-time basis
• Being a global company, their Platform has to be consistent and 100%
available 24x7 – no downtime
• Must be able to simultaneously ingest (stream) and update data in the
same cluster
SOLUTION
• MapR was the only distribution that met the mission critical needs of the
customer and also provided the capability to ingest data continuously into
the cluster
• Direct NFS allows data to be continuously ingested directly into their cluster
• MapR-XD’s self-healing capability allowed them to go into production safely
Leading
Credit Card
Company

Provide Customers With a Customized Experience
Provide customers with a personalized and convenient experience
Using ML/AI to bring
customer understanding
to the center of business
processes
OBJECTIVE
• Use full knowledge of customer relationship to inform online interactions.
CHALLENGES
• Need to store 20 trillion records
• Training sample size is 400 million records
• The decision trees contained 2 million possible pathways
• Every combination must be evaluated every time a model is used (~15 billion
combinations)
SOLUTION
• The MapR Converged Data Platform centralizes analytics and operational apps on
one platform allowing Quantium to make one large infrastructure investment
instead of many small silo’d ones. Current cluster has 50TB of memory and 5000
CPUs to process and store 5PB of data

A Winning Approach: Data First

Gartner estimates they solve between
10-100 business problems in three to
five years.
Gartner estimates they solve
between 3-20 business
problems in three to five years.
20%
Contemplators Experimenters
41%40%
Adopters
Uncertain about the
benefits of Data Science.
Desire easy entry
Entry Points in the Data Science Journey
20%
Source: Gartner – Magic Quadrant for Data Science Platforms (2017)

five years.
Uncertain about the
Desire easy entry
Adopters
20%
41%40%
80%!
Source: Gartner – Magic Quadrant for Data Science Platforms (2017)

five years.
Uncertain about the
Desire easy entry
Adopters
20%
Experimenters
41%
AI adoption outside of the tech sector
is stuck here and many firms report they are
uncertain of the ROI
Contemplators
40%
Investment in AI is growing at a high rate,
but adoption in 2017 remains low
AI is only deployed into production
12% of the time

five years.
Uncertain about the
Desire easy entry
41%40%
Adopters
20%
Seamless Data Access
Technical Capabilities (a strong digital foundation)
Leadership From The Top
Key Traits Of A Successful Data Science Approach

If it is ALL about the data,
then it better be about ALL your data.
Seamless Data Access

ML Models Improve when Trained on Larger Datasets
Instead of relying on
assumptions and weak
correlations, presence of
more data results in better
and more accurate models
Source: A Survey of Applications of AI Algorithms in Eco-environmental modelling (2009)

Data Growth Puts A Premium on Efficient Leverage
Source: McKinsey Global Institute: “The Age of Analytics”, Dec. 2016
The amount of data
is predicted to
double every three
years
Data Diversity
EmailsCall Detail
Records
Click
stream
CSV DocumentsData
PDFBilling Data Meta
Data
JSON Network
Data
Mobile
Data
XMLProduct
Catalog
Medical
Records
Text Files VideoText
Messages
Merchant
Listings
Sensor
Data
Server
Logs
Set Top
Box
Social
Media
Audio
4 Zettabytes
of Data
20111986
300 Exabytes
of Data
3 Exabytes
of Data
20192016
2 Zettabytes
of Data

Hadoop + Vendor Approach to Data Science
Requires yet another cluster
Data Science
cluster
Batch
Cluster
Streaming
Cluster
NoSQL
Cluster
On Premises

A Capable Platform With a Strong Digital Foundation
NFS POSIX REST HDFS
MAPR CONVERGED DATA PLATFORM
ON-PREMISES, MULTI-CLOUD, IoT EDGE
FILE
STORE
CONTAINER
STORE
CUSTOM
FILE APPS
METADATA
MANAGEMENT
JSON HBASEKAFKA
HADOOP & SPARK
APPS
REAL-TIME
BI APPS
STREAMING
APPS
IoT/EDGE
SQL
OPERATIONAL DATA
HUB
CDC
CONTEXTUAL
USER
EXPERIENCES
CORE BUSINESS
APPS
SINGLE
VIEW
IOT

Real-time Machine Learning Pipelines
A Robust Microservices Framework
Event Streams
• Persistent
• Infinitely replicable
• Re-playable
Compare model
results live!
M
Model A
M
Model B
Persistent
Client & Application
Containers

Advice For Leadership
Avoid
• Creating new silos
• Looking for a one-trick pony
• Adopting tools that have
unwieldy install, integration,
and configuration processes
• Tools that don’t scale to
broader enterprise use
• Ensure secure role based
access to all data
• Adopt tools that meet the
needs of a broad range of
Data Science Teams
• Encourage adoption by
making things easy, secure,
and complete
Important

Data Science @ MapR

The MapR Data Science Vision
A Holistic Approach To Self-Service Data Science
MAPR DATA SCIENCE REFINERY REFINERY DATA SCIENTISTS
Data Scientist led product-and-
services offerings including Quick
Start Solutions (QSS) & Training
REFINERY PARTNERSHIPS
Expand on what we offer in-
product to meet the needs of all
data science teams
An easy-to-deploy, secure, and
extensible data science offering
that leverages all existing platform
assets

MapR Data Science Refinery
Provides the ability to work across many
engines in one visual space
• Apache Spark: Spark Streaming, SparkSQL, SparkR, and
PySpark
• Apache Hive
• Apache Pig
• Apache Drill
• Python
• Shell access to MapR-FS
• Programmatic access to MapR-DB and MapR-ES in Spark
Pluggable Visualization Available via Helium!
An Enterprise-ready Data Science Notebook
MAPR
POSIX CLIENT
FOR CONTAINERS
MAPR
CONVERGED CLIENT
FOR CONTAINERS

MapR Data Science Refinery Benefits
Easy to Deploy
• A Docker Image includes all the necessary bits - no more,
no less - required to leverage MapR as a persistent data
store for your data science output.
• Available on DockerHub
Secure
• Authentication occurs at a container level to ensure
containerized applications only have access to data for
which they are authorized.
• Communications are encrypted to ensure privacy when
accessing data in MapR.
Extensible
• A Dockerfile is also available on GitHub, allowing you to
further customize the image as needed to support your
specific application needs.
• The Helium Framework enables pluggable visualization
Leverage Locally, On-premise, or in Cloud
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES
High Availability Real-time Unified Security Multi-Tenancy Disaster Recovery Global Namespace

Partner Integration: An Example
We’re enabling our partners to integrate with and use this product
DataScience.com Platform
Services
MapR DSR
Zeppelin Livy
JDBC
MapR Clients

Demo: Ease of Deployment &
Data Exploration

Demo: Ease of Deployment
What’s in the command
docker run --rm -it --cap-add SYS_ADMIN --cap-add SYS_RESOURCE --
device /dev/fuse --memory 0 -e MAPR_CLUSTER=my.cluster.com -e
MAPR_MEMORY=0 -e MAPR_MOUNT_PATH=/mapr -e
MAPR_TZ=America/Los_Angeles -e MAPR_CONTAINER_USER=mapr -e
MAPR_CONTAINER_UID=5000 -e MAPR_CONTAINER_GROUP=mapr -e
MAPR_CONTAINER_GID=5000 -e
MAPR_CLDB_HOSTS=172.24.8.195,172.24.11.200,172.24.10.4 -e
MAPR_TICKETFILE_LOCATION=/tmp/maprticket_5000 -e
ZEPPELIN_SSL_PORT=9995 -e HOST_IP=172.24.11.62 -e
MAPR_HS_HOST=172.24.8.195 -p 9995:9995 -p 10000-10010:10000-10010 -v
/tmp/maprticket_5000:/tmp/maprticket_5000:ro -v
/sys/fs/cgroup:/sys/fs/cgroup:ro maprtech/data-science-
refinery:v1.0_6.0.0_4.0.0_centos7

MAPR_CLDB_HOSTS=172.24.8.195,172.24.11.200,172.24.10.4 -e
MAPR_HS_HOST=172.24.8.195 -p 9995:9995 -p 10000-10010:10000-10010 -v

How is Security Handled?
$ maprlogin password
[Password for user ’jane' at cluster 'my.cluster.com': ]
MapR credentials of user ’john' for cluster 'my.cluster.com' are written to '/tmp/janes_ticket’
Job submits as ‘jane’

Why Livy?
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES
MAPR CONVERGED DATA PLATFORMHTTP (RPC)
Advantages over native Spark Interpreter:
• Jobs are submitted in YARN cluster mode
• Spark context can be shared
• Support for Spark Dynamic Resource Allocation

Demo: Extensibility &
Collaboration

Demo: Extensibility & Collaboration
Collaboration
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES

Collaboration
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES
MAPR
POSIX CLIENT
FOR CONTAINERS

docker run --rm -it --cap-add SYS_ADMIN --cap-add SYS_RESOURCE --device
/dev/fuse --memory 0 -e MAPR_CLUSTER=my.cluster.com -e
ZEPPELIN_NOTEBOOK_DIR=/mapr/my.cluster.com/user/mapr/zeppelin/shared-
notebooks/ -e MAPR_TZ=America/Los_Angeles -e
MAPR_CONTAINER_USER=mapr -e MAPR_CONTAINER_UID=5000 -e
MAPR_CONTAINER_GROUP=mapr -e MAPR_CONTAINER_GID=5000 -e
MAPR_CLDB_HOSTS=172.24.8.195,172.24.11.200,172.24.10.4 -e
MAPR_HS_HOST=172.24.8.195 -p 9995:9995 -p 10000-10010:10000-10010 -v
/tmp/maprticket_5000:/tmp/maprticket_5000:ro -v /sys/fs/cgroup:/sys/fs/cgroup:ro
maprtech/data-science-refinery:v1.0_6.0.0_4.0.0_centos7

Demo: Extensibility
Adding Deep Learning libraries to the container

Demo: Extensibility
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES
Compute Persistent Storage

Demo: Extensibility
CLOUD-SCALE
DATA STORE
MAPR-XD
OPERATIONAL
DATABASE
MAPR-DB
EVENT
STREAMING
MAPR-ES
Compute Persistent Storage
What if this was a
box of GPUs?

A Final Comparison
Traditional Hadoop Vendor
BatchCluster
StreamingCluster
NoSQLCluster
On Premises
Data
Science
cluster

Q&A
ENGAGE WITH US
@mapr
rsilver@mapr.com

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