How to Innovate Your Software with ML? - Amazon Web Services... · project management scrum skill management budget management sprint pics Project Portfolio Management project score

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T

How to Innovate Your Software with ML?

Dr. Jürgen NützelCEO4FriendsOnly.com Internet Technologies AG

Thomas SchlerethManaging Director Can Do GmbH

Markus OponczewskiDirector Business Unitfme AG

Raul FiruCTOHaufe Group


The Lack of skilled Professionals – From Crisis to Opportunity with AIThomas Schlereth, Can Do GmbH

AWS Conversational ML Services and ECMMarkus Oponczewski, fme AG

Behind the Scenes – Haufe’s AI JourneyRaul Firu, Haufe Group

How QuickSight visualizes KPIs of the E-Commerce Recommendation EngineDr. Jürgen Nützel, 4FriendsOnly.com Internet Technologies AG


How QuickSight visualizes KPIs of the E-Commerce Recommendation Engine

Dr. Jürgen NützelCEO4FriendsOnly.com Internet Technologies AG

AWS Summit Berlin 02/2019 Dr. Jürgen Nützel, [email protected], 4FriendsOnly.com Internet Technologies AG 4

How QuickSight visualizes KPIs of

the E-Commerce

recommendation engine

4FriendsOnly.com Internet Technologies AG– Spin-off of Fraunhofer IDMT & university

– CEO, lecturer and main owner: Dr. Jürgen Nützel

– E-Commerce experts (> 10 years)

– We focus also on

• Mobile development

• Cloud-computing (AWS since 2013)

• Digital transformation

– Amazon AWS partner since 2017

– AWS user group lead


One of our Customer – Papier LIEBLa B2B Office Wholesaler from Regensburg …


We develop and run their

B2B online shops on AWS:

www.liebl.de,

procurement.liebl.de

… asked us for a

recommendation service

which categorizes online

shoppers very fast.

Recommendations in the ShopAWS Summit Berlin 02/2019 Dr. Jürgen Nützel, [email protected], 4FriendsOnly.com Internet Technologies AG 7

In the online shop

with active

debugging info

How we get Recommendations• Assumption

Users with similar behaviour (in the shop) have similar interests

• DataUser’s click stream in the shop

Mobile or desktop user

Shopping time (Friday and weekend is different, especially in B2B)

• CalculationUsers similarity matrix. Similar means having same products clicked/bought

The products the most similar users have clicked/bought most

• ImplementationSimilarity matrix will be updated in memory in real-time (using Node.js)


Setup we start with


E-Commerce-Cluster calls

protected API to receive

recommendations

Browser sends click stream data

to the server{"action":"addToCart","data":{"sku":"50189","

price":"3.27","quantity":"5"}}

Drawing made with: https://cloudcraft.co

https://cloudcraft.co/

How QuickSight comes into play


We can pre-configure any

diagram to show the shop

manager all wanted KPIs (not

only recommendation related)

QuickSight is not available in

Frankfurt. We had to

replicate the data to Ireland

We manage the data in QuickSightAWS Summit Berlin 02/2019 Dr. Jürgen Nützel, [email protected], 4FriendsOnly.com Internet Technologies AG 11

We prepare analyses and dashboardsAWS Summit Berlin 02/2019 Dr. Jürgen Nützel, [email protected], 4FriendsOnly.com Internet Technologies AG 12


Embedding QuickSight Dashboards(using the Enterprise edition)

We show our customer the

success of the

recommendation

Thank you

Follow us on Meetup:

https://www.meetup.com/de-DE/Ilmenau-Amazon-Web-Services-Meetup/

Dr. Jürgen Nützel

CEO

[email protected]

www.4fo.de/en


https://www.meetup.com/de-DE/Ilmenau-Amazon-Web-Services-Meetup/

http://www.4fo.de/en


The Lack of skilled Professionals –From Crisis to Opportunity with AI

Thomas SchlerethManaging Director Can Do GmbH

THE LACK OF skilled professionals

From crisis to opportunity with AI

More than 80,000 IT specialists are

desperately sought in GermanyWAR for Talents

Can Do GmbH Provider of standard software

for professional resource

management and skill-based

resource planning

CustomersUsing Can Do, more than 80

companies - mostly corporate

groups in DACH - benefit from

an optimized deployment of their staff

• Best possible deployment of staff

• Comprehensive risk management

• New quality of resource planning due to skills

• Recommended for action given by Can Do AI

• Company-wide project management &

resource management

PROJECT SUCCESS

thanks to Can Do

project management

scru

m

skill managementbudget management

sprint

eppi

cs

Project Portfolio Management

project score

proj

ect c

osts

bottlneck resource permanent target-actual comparison

jira integration

time recording

demand management

base load

agile organization

Hybrid Project ManagementW

ater

mod

el®

-dy

nam

icca

paci

ty

bala

ncin

gm

etho

d

plan

cos

ts

algorithm: planning with

inaccurate data

wor

kloa

d

skill

cate

gory

interfacesmanagement dashboards

skill based resource management

RANGE OF SERVICE

ARTIFICIAL INTELLIGENCE

vacation planning

approval procedure

capacity

analysis

real

-tim

e co

mp

uti

ng

ai: expert system

cost report

pull-off feature

project progress

performance analysis

baseline analysis

portfolio management

reso

urc

e m

anag

emen

t

epics

overload control

inter- and

Cross project

linkages

Gaussian standard normal distribution

user-centric

app model

role

-spe

cific

user

-frie

ndly

milestones

Gantt chartGantt chart

sub-projects

Pro

babi

lity

of o

ccur

renc

e of

pro

ject

s

Control over approx. 20 risk types

use case oriented

Can

Do

IT TAKES A LOT OF EFFORT AND

MONEY TO MAKE THIS FLY

MASSIVECOMPUTING POWER REQUIRED

1 ressource with 10 inaccurate planned working packages

Example: Planing with inaccurate data:

Inaccuracy Calculation Layer ( Gauss ) & Watermodel® algorithmAPPROX. 500.000 ITERATIONS

CPU: 32 CPU-Cores > 2 GHz

RAM: min. 64 GB

RECOMMENDING AI Learning System for indepth

analysis

Monitoring AI Pattern Matching

AWS

o Trial and Error

o Preparation of a requirement specification:

• Hardware Costs

• Licensing Costs

• Implementation Costs

• Training Costs

• Costs for software & hardware operation

COST & EFFORT COMPARISON

TOTAL USER COSTS

On Site

o Selection process: RFI, RFP, long list, short list

o Preparation of a requirement specification:

• Hardware Costs

• Licensing Costs

• Implementation Costs

• Training Costs

• Costs for software & hardware operation

faster, better, cheaper

ML in AWS is

Thomas Schlereth

Managing Director

Can Do GmbH

thomas.schlereth@

can-do.de

www.can-do.de

Ensuring the best possible deployment of your staff!


AWS Conversational ML Services and ECM

Markus OponczewskiDirector Business Unitfme AG

AWS Conversational ML Services and ECMAWS Summit 2019 Berlin – Software Innovation Forum

Markus Oponczewski

[email protected]

@moponczewski

fme – Who we are

BS, MUC, FFM – Danbury (US) – Cluj-Napoca (RO)

250+ Talented People & Dedicated Professionals

Enterprise Content / Digital Transformation / Cloud Computing

Cloud Process Expertise: Cloud-native, DevOps, CloudOps

Cloud Platform Expertise: AWS & Pivotal

What is an ECM System ?

30

Think of…

- Document Management

- Content & Media Handling

- Sensitive Data Protection

- Global Collaboration & Workflow

- Archival Solutions (DSGVO)

- Enterprise Requirements

The Challenge

#6120 © fme AG

Extend System Operability

without changing the System !

Extend System Functionality

without changing the System !

Amazon Alexa

Comprehend, Translate, Polly

Demo Time

32

Watch The Full Videohttps://www.youtube.com/watch?v=jKGAeEI8m5g

© fme AG

Show Me The Folder

Open The Document

Read/ Translate The Document

Read/ Translate The […] Section

Read/ Translate Page Number […]

Translate The Document Into […]

Translate And Save in DCTM

Extend Operability with Alexa & Extend Functionality with Comprehend, Translate and Polly

Interact with

• Alexa

• Comprehend

• Translate

• Polly

The Architecture

33 © fme AG

The Architecture

34 © fme AG

REST-API

-

API

-

SDK

Any Existing or

Legacy System

API Gateway

Other UseCases

Extend Functionality with Machine Learning Services

- Pimp Incident & Ticket System (ITSM)

Extend Operability with Alexa Voice UI

- Chemical & Pharmaceutical Lab environments

#6120 © fme AG

It’s fme you love to work with.

fme AG | Rupprechtstr. 25| D-80636 München | www.fme.de

Markus Oponczewski

[email protected]

@moponczewski


Behind the Scenes – Haufe’s AI Journey

Raul FiruCTOHaufe Group

BEHIND THE SCENES

The haufe group

Why is ai so different?

μ

Foundations vs. what you see

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

M L F R A M E W O R K S &

I N F R A S T R U C T U R E

A I S E R V I C E S

R E K O G N I T I O N

I M A G E

P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N

V I D E O

Vis ion Speech Language Chatbots

A M A Z O N

S A G E M A K E R

B U I L D T R A I N

F O R E C A S T

Forecast ing

T E X T R A C T P E R S O N A L I Z E

Recommendat ions

D E P L O Y

Pre-bui l t a lgor i thms & notebooks

Data label ing (G R O U N D T R U T H )

One-c l ick model t ra in ing & tuning

Opt imizat ion (N E O )

One-c l ick deployment & host ing

M L S E R V I C E S

F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e

E C 2 P 3

& P 3 N

E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C

I N F E R E N C E

Reinforcement learningAlgor i thms & models ( A W S M A R K E T P L A C E

F O R M A C H I N E L E A R N I N G )

“Starting at the bottom”

μ

Semantics analytics based recommender

“Starting in the middle”Organizational analytics – semantics based clustering

“Starting at the top”NLP Based search for all haufe content

Takeaways

“It is difficult to make predictions, especially

about the future.”


AI and Machine Learning ISV Event in Germany

Deep dive workshops

Separate Business and Technical tracks

Timing: May/June 2019

Attractive prizes for selected participants

Networking opportunity

S U M M I TB ERL I N

Documents

How to Innovate Your Software with ML? - Amazon Web Services... · project management scrum skill management budget management sprint pics Project Portfolio Management project score