machine learning for z/os

© 2017 IBM Corporation

IBM Analytics

A Complete Picture of zAnalytics

DB2, IDAA, & Watson Machine Learning for System z

Cüneyt GöksuzAnalytics Technical Leader, MEA & Turkey

IBM Analytics Platform

© 2017 IBM Corporation2

Topics

zAnalytics Strategy

Introduction to Machine Learning

DB2 for z/OS Update

Integrating the DB2 Analytics Accelerator

Machine Learning for z/OS


ETL

pro

cess

Data Replication

Where customers currently find themselves

Distributed Data Warehouse(s) Data Lake / Hadoop

Mainframe

CubesMartsTables

1Mainframe as transactional backbone– business critical and reliable

Data Warehouse for BusinessIntelligence purposes

Data Lake Concept to merge structuredand unstructured data

2

3

Web

services


Data Replication

The challenges of today‘s approaches


Mainframe

CubesMartsTables

1Latency caused by an overnight batch and ETL process not acceptable for real-time requirements of business units

Complexity of managing data movement, marts, cubes andsecurity is permanently increasing and no longer manageable

Overall cost to overcome latency and to manage andadminister complexity is exploding

2

3

ETL

pro

cess W

eb services


The IBM approach to modern, real-time analytics


Mainframe

CubesMartsTables

1

Direct, real-time access w/o any latency to business critical mainframe data and other loaded data- Savings in Offload-MIPS, Storage, Admin Efforts

Perform transformations on accelerator to load Data Warehouse faster- Provide relief for DWH through optimized report balancing- Savings in ETL cost and growth avoidance

Simpler and faster data provisioning into Data Lake Concepts through leverage of SPARK technology

2

3

ETL

pro

cess

Rules Application

& Scoring

HTAP

• Extreme performance:

Potentially for existing, but

more importantly for new

workload

• Use of non DB2 data

(IMS, VSAM, external data)

• Use of archives /historical data

• In-database transformation

• In-database analytics allowing

modelling

• Hybrid cloud

DB2 Accelerator personalities

Cluster

node

node


IBM Analytics

Introduction to Machine Learning


The focus on machine learning

Gartner identifies

Machine Learning as the

Top Trend in IT for 2017 and

at the top of every CIO's

strategy & budget

Source Gartner

Machine learning

segment of the cognitive

computing market forecast to

grow from $6 billion in 2016 to

$52 billion in 2021 with a CAGR

of 53.5% for 2016-2021

Published date: 05/02/2016 Source:

Mindcommerce

Data scientists

are the superheroes and

unicorns of today's business.

But data scientists are only

human, and they are reaching

the limits of productivity with

current processes.

Published date: 02/11/2016 Source:

Forrester


“Ability of computers to learn without being explicitly

programmed”

Machine Learning - What and Why?


Machine learning is everywhere,

influencing nearly everything we do…

Netflix personalized movie recommendations

7 out of 10 financial customers would take recommendations from a robo advisor

Machine Learning Basics

Identifies patterns in historical data

Builds behavioral models from patterns

Makes recommendations

The data (operational & historical) is used to “train” a model


IBM Analytics

DB2 for z/OS Update


Strategic Areas

Analytics IDAA, HTAP

In-transaction analytics, machine learning

Cloud Self-service for app developers

Hybrid cloud

Simplification, self-management

Modern Application Development Mobile, IoT

SAP

Application portability, open source integration

Core Technology Security

Performance, scalability, in-memory, z hw integration

Continuous availability

Foundation: internal dev/test tooling modernization for DevOps

Core Technology

Analytics

Foundation

CloudModern

App


Scale and speed for the next era of mobile applicationsSuper fast ingest rate -- over 11 Million Inserts per second for IOT, Mobile and Cloud*

280 trillion rows in a single DB2 table, with agile partition technology

DB2 12 supports true Enterprise scale next gen apps

In-Memory databaseAdvanced in-memory techniques in DB2 12 mean faster transactions at lowered overhead, up to 20% CPU savings on index

lookups

Deliver analytical insights faster2-10x improvement for modern analytics workloads

Individual modern analytic queries may see up to 100x improvement**

Spark Integration with Enterprise QOS

API Economy - Next Gen application support540 million transactions per hour through RESTful web API

Redefining enterprise IT for digital business and the mobile app economy

DB2 12 for z/OS – Oct, 2016 GA

*: Under dedicated environment using 12 way data sharing on z13, insert against one table (PBR/Member Cluster) from zLinux clients. All partitions were

GBP dependent and logging enabled. Our record is, 11.7 million insert per second without index, 5.3 million insert per second with index defined.

** Modern analytics queries evaluated include SQL constructs such as UNION ALL, outer joins, complex expressions (CASE, CAST, scalar functions etc)


DB2 Strategy: Support the Next Wave of

Applications

• Continue as the OLTP leader

• Support Real-time Cognitive Analytics

• Support for mobile and NoSQL apps

• Enable for cloud and hybrid cloud applications

– Cloud services for DB2

• DevOps for modern agile/rapid application development

• Provide new SQL features

4.8M app connections on a single DB2 instance

Unmatched quality of service

Open access to modern apps with data security

Mobile DataCloud

Transparent

Archiving

Real Time Analytics

Location Based

Data

IDAA

Hybrid Transactional/

Analytics Processing


What is it? • A major advance in table management in DB2

1. Breaks the existing limitations in terms of table size and partitioning • 280 trillion rows is impressive by anyone's standard• One DB2 12 table can contain every email sent on the entire Earth for 3 years• One DB2 12 table can contain every text message sent on the entire Earth for 32 years

2. A massive improvement in simplifying DB2 table management for our users• Non-disruptive partition size increases for any partition in a table• Ability to insert new partitions in the middle of a table• Much greater partition independence, so if you change table definitions, you do not need to process the entire

table

DB2 12 Agile Partition Technology Table

Partitions

Huge applicability to next generation workloads that require large size and flexibility

Much greater efficiency for administrators, and elimination of application downtime

ATM

Transactions


What is it? • Obtaining data for immediate use or storage in a database

Examples:• Tracking clicks on a website• Capturing every text message in a wireless network• Capturing Call Data Records in a mobile network• Tracking events generated by “smart meters” in a monitoring system• Capturing data from hundreds of thousands of mobile application users• Social Media and SaaS applications generate massive amounts of real-time data, as they have matured storing and

analyzing this data has become critically important

DB2 12 Super Fast Ingest RateDB2 clients are creating ever more data at increasing speedsThere are a growing number of consumers of that data — both operations and analytics

Next generation applications demand Ultra high ingest rates with enterprise scale

Qualities of Service only found in DB2 on z/OS,

All while being able to query the data using standard SQL!


What is it?• An in-memory database (IMDB) is one that primarily relies on main memory

for data storage (as opposed to disk)

What does this mean? • New workloads (and existing workloads) can benefit tremendously

• Ex. Fast lookup of transactional data from mobile devices• The DB2 Lab has measured up to a 23% reduction in CPU

on existing workloads

• Memory on the z platform is getting larger and cheaper• We expect more growth in future hardware

• DB2 12 exploits large memory for improved performance and CPU reductions• Larger buffer pools to reduce I/O• New memory optimized structures to speed up performance

DB2 12 is an In-Memory Database

In-Memory database means faster transactions and queries with lower CPU overhead


DB2 Data as a Service DB2 Cloud/Mobile modernization with RESTful APIs and JSON

Enterprise

Apps

Enterprise

Data

Enterprise

Transaction

Processing

Systems of Record

Cloud APIs

Mobile-Optimized APIs

Cloud-based

ServicesEnterprise

Systems

Integration

z/OS Connect or DB2 native REST

Serving mobile data directly from z/OS is 40% less

expensive than exporting to a system of engagement

CICS,

IMS

Batch,

WAS

• Many modern application developers work with REST services and JSON data formats

• DB2 12 (and DB2 11 APAR PI66828) ship a Native DB2 REST service• Easier DBA management of DB2 RESTful services, means easier adoption

• z/OS Connect Enterprise Edition (zCEE) integration

Native DB2 REST service provider now available


DB2 12 Continuous DeliveryEnd of 2015: Reality check for DB2

We deliver most of our new function in a new release ~every 3 years but many are on 4 year cycles, hence

the interest in skip release migrations

Industry and customer trend is to move away from monolithic code delivery towards continuous delivery model

Next Gen apps and workload growth needs to be our focus

Modernize our delivery technique

We changed !!

We dedicated ourselves to going forward with Continuous Delivery

Customers will see a single maintenance stream for DB2 12, with consumable new function

delivered into that

Continuous release cycles : DB2 V12.1, V12.2, V12.3, …. As you order V12, you get what is available

Faster delivery of easily consumable new features. Items delivered when ready, not three years in the

future

Clients will be able to adopt new technology sooner while maintaining complete control

We will pull DB2 customers forward with our exciting new functions

Integrates perfectly with the new DevOps methodologies being adopted by our clients


IBM Analytics

DB2 Analytics Accelerator Update and

Integrating with Machine Learning

20

IBM Machine Learning for z/OS

Integrating the DB2 Analytics Accelerator with ML for z/OS


ML for z/OS enhances the analytics

solution as follows:

Providing tooling for entire data

scientist life cycle:

• Model build, train, validation, ...

• Health monitoring

• Model optimization

Enables scoring on z/OS platform

Leverages Spark MLlib and Spark for

z/OS as runtime engine

. . .

IBM DB2 Analytics Accelerator

DB2 Analytics Accelerator enhances

the analytics solution as follows:

Supporting data engineering tasks:

• z/OS data can be prepared via in-

DB transformation (AOT usage)

SQL access to z/OS data can be

accelerated

HPSS archiving can be used to

accomodate huge amount of z/OS

data

. . .

22


DB2 IDAA

data dataasynchronous

replication

most recent committed

data available?

yes

no

Write

requests

OLTP read

requests

OLAP read

requests

wait for

given

time

period

most recent

committed

data

required?

yes

no

initiate

apply

Reading most recent committed Data during asynchronous Replication

called ZERO LATENCY

24


DB2 Analytics Accelerator & ML for z/OS – Complementing Values

Situation #1

Small amount of z/OS

data

zIPP / memory with

sufficient capacity

Scala, Python for data

prep required

Jupyter Notebook of

ML for z/OS used

No SQL skills or SQL

not appropriate

...

ML for z/OS

DB2 Analytics AcceleratorDB2 Analytics AcceleratorDB2 Analytics Accelerator

ML for z/OS ML for z/OS

Situation #2

Large amount of z/OS

data

DataStage (or similar

tool) already used

SQL accepeted for data

prep

Z capacity insufficient

for data prep

ML for z/OS Jupyter

notebook used for

addtl. data prep (similar

to SAS data cube build)

Supporting broader

data lake topologies

Accomodating more

data due to archiving

Need for limited R

support

...

Situation #3


data

DB2 Analytics

Accelerator already

deployed

Addtl. points similar to

situation #2

Need for limited R

support

...

Situation #3


data

PMML needed

Batch scoring

SPSS Modeler and

SPSS C&DS already

used (integrates with

the Accelerator)

Interest in in-DB

Analytics

Need for limited R

support

Supporting broader

data lake topologies

...


IBM Analytics

Machine Learning for z/OS


Let’s repeat the ML Concepts

Data is used to “train” a model

The data is historical data and has known outcomes

The model can be tested by a subset of the same data

Usually a percentage of the historical data is held back

A known set of parameters is ”scored” using the model

The result is checked against the actual result from the

historical data

Best practices suggest that the accuracy of the model be

further validated / evaluated by known data


A feature is a piece of information that might be useful for prediction

Example, predict the probability of a customer buying a product

Labeled data is the desired output data

Example, 1.0 representing a customer has bought a product; 0.0 representing NOT

Machine Learning 101 : Supervised Learning

GENDER AGE MARITAL_STATUS PROFESSION CUSTOMER_ID LABEL

F 24 Married Retail 4003 1.0

M 43 Married Trades 4004 1.0

F 43 Unspecified Hospitality 4005 0.0

F 43 Unspecified Sales 4006 1.0

M 28 Single Trades 4007 1.0

Feature Feature Feature Feature NOT a feature Label


Training a

model

Feature

Engineering

Feature

EngineeringScoring

Labeled examples

Training

Scoring

Newdata

Model

ModelPredicted

data

DeployData Science Experience

Operational system

Dev

Ops

Machine Learning 101 : a TrainOps (DevOps) story



Training and scoring on z using Spark for z/OS as the backend data processor

Customers can train models best fit for their business with the data on mainframe

Customers can deploy the ML models and perform online scoring within transaction on mainframe

The machine learning workflow

Online scoring on z – REST API

Training on z

z/OS data – DB2, IMS, VSAM, IDAA etc.


Machine Learning for z/OS Architecture Diagram

Notes:

• Bundled software are packaged and

delivered with MLz (outlined in Green)

• MLz component are unique value

delivered with MLz (outlined in Blue)

• Pre-requisite software are what

customers need to separately procure

(for example, z/OS Spark Cluster =

z/OS Platform for Apache Spark

ordered through ShopZ (outlined in

Red)

• Data sources (outlined in Yellow)

represent sample sources that can be

accessed through MDSS

z/OS Liberty

Application Cluster

Ingestion

service

Training

service

z/OS Spark Cluster

Ingestion lib Pipeline libService Metadata

ML models

DB2z

MDSS

driver

IBM Machine Learning UI

Jupyter Notebook / Visual Model Builder

Model Management / Model Deployment / Monitoring

Bundled software

MLz component

Pre-requisite software

z/OS Data sources

zLDAP

RACF

(optional)

Auth

Service

Kubernetes Docker

GlusterFSLinux (x86 or Linux on z)

z/OS

Scoring serviceIMSVSAM

Jupyter Kernel Gateway

Repository

Service

Deployment

Service

(Model

Monitoring)

LDBM

Jupyter

Notebook

Server

DB2 SMF

CouchDB

(NoSQL

Metadata)

Apache Toree

z/OS Spark

In

Local Mode

CADS/HPO lib

DB2 JDBC driver

Python

2.7

Brunel (Visualization)

PHOTO CREDIT: Kyle Harris

Machine Learning is to the 21st century what Industrial Revolution

was to the 18th century.

machine learning for z/os

Data & Analytics