Embedded-ml(ai)applications - Bjoern Staender

1
September 12, 2018
the first Step towards Autonomous Software
Examples and Use Cases
Bjoern.Staender@oracle.com
Embedded Machine Learning

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.

• Big Data: Massive volumes of data now available
in next generation data lakes to drive predictive
analytics
• Machine Learning: Mainstreaming to drive digital
transformation and competitive advantage
• Cloud: Instant, elastic compute on infinite storage,
all available on demand driving new cloud
economics
Convergence of Big Data, Machine Learning and Cloud
3
Cloud
Big
Data
Machine
Learning

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. 4
Technological Revolution enabled by Cloud Economics
Data Lakes Driven by a Technology and Economic Revolution
Relational and File Storage
Fixed clusters – compute and storage
Batch Processing with MapReduce
Complex Big Data Ops with Open Source
Cloud Storage
Real Time Processing with Spark
Clusters on demand; elastic compute / storage
Managed Service in the Cloud
Traditional Big Data Processing Cloud Native Data Lake Processing
Fixed Up Front Cost, Batch, Complex Pay as You Go, Interactive/Real Time, Managed

Data Lake Challenges
5
Data Ingestion
Data Discovery
and Governance
Data Processing
and Analytics
• Many different data
sources - OLTP, DW,
Apps, Streams, Events
• Batch vs Streams
• Inconsistent Formats
• Different technology
infrastructures
• What data is available
for analytics and data
science
• What is the providence
of that data
• How is that data
secured?
These challenges require a DATA LAKE PLATFORM
Operational
Challenges
• Expensive and
Scarce Talent
• Hard to Manage
• Maintaining QoS at
Scale
• Complex to set up
clusters for
processing
• Batch analytics too
slow for real decision
making
• Driving performance
in subject area data
marts

Industry Consensus on Cloud Based Data Lake Components
6
Streams
Batch
Events
Data Integration Data Processing Governance Analytics & Data Science
Data Catalog
Index
Data WarehouseObject Storage
Process
Analytics
Data Science
Data Lake Components
Security
Cloud Infrastructure as a Service

Sample Data Science Use Cases
7
Marketing
Response Models
Scheduled Jobs
Customer churn
APIs
Text sentiment
analysis
Reports
Lifetime Value
Apps
Computer vision
and image tagging
Apps
Transactional data
ETL
Scheduled Jobs
Forecasting
Reports
Risk management
with machine
learning
APIs
Recommendation
engines
APIs
Data discovery and
auditing
Reports

What’s Holding Companies Back?
8
Data Scientists
Cannot work
efficiently
App Developers
Cannot access usable
ML
IT Admins
Too much time on
support
• Lengthy waits for
resources and data
• Difficulty collaborating
with teammates
• Long delays of days or
weeks to deploy work
• Many tools to manage
• No access to well-trained
models
• Access points not flexible
for deployment in all
scenarios
• Scalability of deployment
left out to the app
developer
Business
Executives
Do not see full ROI
• Growing list of open source
tools
• Continually building and
updating environments
• Limited standardization
across workflows
• No transparency into work
• No model integration
with decision making
systems
• Unable to access or
share outputs
• Difficult to collaborate
with data scientists
Despite the promise of data science, and huge investments in data science teams, inefficient
workflows are holding companies back from realizing the full potential of machine learning.

Data Science Cloud Workflow
9
Reproducibility
Data
Versioning
Code
Versioning
Model
Versioning
Environment
Management
Model Deployment
Operationalize Models as
Scalable APIs
Model Management
Monitor and Optimize Model
Performance
Data Exploration
Collaborative Data Analysis /
Feature Engineering
Model Build and Train
with Open Source Frameworks
Collaborators
∙ Data Scientists
∙ Business Stakeholders
∙ App Developers
∙ IT Admins

Data Science Cloud Key Components & Benefits
10
Collaborative
Project driven UI enables teams to easily
work together on end-to-end modeling
workflows with self-service access to data
and resources
Integrated
Support for latest open source tools, version
control, and tight integration with OCI and
Oracle Big Data Platform
Enterprise-Grade
A fully managed platform built to meet the
needs of the modern enterprise
Core Benefits:
Oracle Data Science Cloud
Oracle PaaS & IaaS
Projects Notebooks
Open Source
Languages &
Libraries
Version
Control
Use Case
Templates
Model
Build & Train
Self-Service Scalable Compute (OCI)
Object
Store
Catalog Data Lake Streaming
Autonomous
Data Warehouse
Model
Deployment
Model
Monitoring
Access
Controls &
Security

Data Science Cloud - INTEGRATION
11
● Platform supports a wide range of open
source tools, libraries, and languages to
tackle different use cases
● Native support for most popular version
control providers (Github, Gitlab, and
Bitbucket) ensures all work is synced
across the platform
● Tight integration with OCI and Oracle Big
Data Platform provides data scientists
with self-service access to scalable
compute and the data they need to get
to work quickly
Data
Analysis, ML,
AI
Version
Control
Tools &
Languages
Visualization
Use the Best of Open Source
Easily Access Data and Compute
Streams
Batch
Data
Warehouse
NoSQL
Databases
Self-Service Scalable Compute (OCI)
Object
Store
Data
Lake
Spark Catalog

Data Science Cloud - COLLABORATION
12
● Project-driven UI simplifies data science
operations and enables teams to work
together
● Built-in version control ensures all data,
code, and models can be tracked and
reproduced
● Granular access controls enable
managers or admins to control who has
access to projects and data
● Support for teams to collaboratively
build, train, deploy, and manage models
from a central workspace

Data Science Cloud - ENTERPRISE-GRADE
13
Fully Managed Highly Available
• Fully managed platform built on Kubernetes
• Platform is highly available — ensuring
anytime, anywhere availability and access
• Support for large teams with containerized
workloads, preventing resource contention
on a scalable cluster
• Integration with Oracle IDCS enables robust
access control management
• Designed to leverage high performance
Oracle Cloud Infrastructure
Scalable Secure
A
D1
A
D2
A
D3
PaaS
IDCS

Mixing Baking Frosting
Embedding Machine Learning in Business Processes …
Let’s take an example that we are all familiar with…Baking a Cake !
Cake Batter Baked Cake Finished Chocolate Cake
Ingredients

We follow the exact recipe & process each time
but why don’t we consistently get perfect cakes ?
CrackedCollapsed
BurntUndercookedShrunk
ChewyDry
White Spots

Too many factors that could contribute to the problem
FrostingBatter Baked Cake Finished Cake
- Consistency ?
- Lumps ?
- Color ?
- Texture ?
- Cracks ?
- Shrinkage ?
- Collapsed ?
- Dryness ?
- Color ?
- Bake Level ?
- Sweetness ?
- Consistency ?
- Appearance ?
- Texture ?
- Temp ?
- Taste ?
- Frosting integrity ?
- Temp ?
- Taste ?
- Expiry Date
- Nut free facility ?
- Organic Milk ?
- Expiry Date ?
- Milk Fat ?
- Expiry Date
- Cage Free ?
- Color ?
- Size ?
- Coarseness ?
- Sweetness ?
- Color ?
- Organic?
- Rotation Speed?
- Stop/start
- Gradual/sudden
changes
- Pre-heat?
- Door Open/close?
- Temp?
- Humidity?

The Root Cause could be a combination of 5M factors
Man Machine
Method
Materials
Management
CAUSE
“Given the sheer number and
complexity of production
activities that influence yield
& quality, manufacturers
need a more granular
approach to diagnosing and
correcting process flaws.”

Every Manufacturer has the same problem
“Data Rich…Information Poor”
“The inability for manufacturers to get actionable insights from data across
Information Technology (IT) and Operational Technology (OT) systems”
Source: Processengineering.co.uk

Operational Report/Standard BI vs. Actionable Intelligence
An Example…
Operational/Standard BI Report
‘What’ Analysis
68% of Batches of Chocolate Cakes made
between Jan 1 and Jan 28 had Excessive
Cracks after Baking Operation.
What everyone has today

Operational Report/Standard BI vs. Actionable Intelligence
An Example…
Operational/Standard BI Report
‘What’ Analysis
Deep Insights
‘Why’ Analysis
Cracks after Baking Operation.
What everyone has today What everyone wants
Cracks after Baking Operation and:
• Eggs were added after sugar @ Mixing Step
• Batch was made in the afternoons
• Sugar Quantity usage between .5 and .7 lbs
• Sugar Lot Number = LT-PG-0001
• Blender Max. Speed = 590 RPM @ Mixing Step
• Oven Average Temperature > 350 F @ Baking Step
IT Data
(ERP, MFG, SCM)
OT Data
(machine/sensor)

Why is this analysis hard ?
1. We need to contextualize machine/sensor data with Enterprise Apps data
2. We need to find hidden Patterns and Correlations in large datasets that affect Outcomes
Example of a Contextualized Data Set
When Oven Average Temperature was above 350F :
Work Order was WO88373 Operation Step was Baking Operator was David Cooper
Shift was 2nd Shift Sugar Lot was LT-PG-0001 Sugar Supplier was White Crystals Inc.
Quality/Test
Results
Bills of Material
Routings
Operator
SkillsCosts
Products, Parts
& Ingredients
Work
Instructions
Suppliers,
Purchase Orders
Maintenance
Work OrdersCustomers,
Sales Orders
Genealogy
Manufacturing
Work Orders
Serial Units
and Lots
Machine /
Sensor Data

Embedded Machine Learning for Manufacturing
Data Lake
Structured
Data
Unstructured
Data
Semi-Structured
Data
PredictionsPatterns & Correlations Genealogy & Trace
ERP
CRM
HCM
SCM
MES
QualityLIMS
Data Preparation & Contextualization
T&A
Machine Learning Model Management
Manpower Machine ManagementMaterial Method
Insight Models Predictive Models Feature Significance Models
Model Deployment
Sensor Time Series (SAX Features)Pre-Seeded & Custom Attributes
Enterprise Applications
Model Definition
Shopfloor Devices
PLC
SCADA
Gateway
Data
Historian
Environment
Data
Model Training Model Performance Evaluation
Root Cause Analysis Impact AnalysisProactive Management
Audio Video Log Files Notes Image

Case studies of applying machine learning technique on manufacturing big data
What Manufacturers were able to achieve using ML
Components that were assembled by
tightening the bolts 12 times had high rate of
failure in the field compared to the ones that
were tightened 13 times during production.
- Missile Systems Manufacturer
There were 200+ parameters that could
influence the vaccine yield. Applying data
mining techniques narrowed the list to 9
parameters that mattered the most.
- BioPharma Manufacturer
Every Chip undergoes 19000 tests. Starting at
the wafer level, analysis of data from the
manufacturing process helped cut down test
time and focus on specific tests. The result was
a savings of $3 million in manufacturing costs.
– Semiconductor Chip Manufacturer
A unique combination of fan speed, temp,
and humidity during painting operation were
the most common factors in vehicles that had
paint crack problems reported by customers.
- Automobile Manufacturer

The preceding is intended to outline our general product direction. It is intended for
Demo Example Screenshot

MESQuality
InventorySensor / IoT Procurement / Supplier
HCM

Lot LT-PG-0001
(Pinion Gear)
Work Orders where
Lot LT-PG-0001 (Pinion
Gear) was used
Material Supplier
(for Pinion Gear)
Finished Products that contain
Lot LT-PG-0001 (Pinion Gear)
Customers and Warehouses that
have Products that contain
Lot LT-PG-0001 (Pinion Gear)

Review all critical events that happened
during the manufacturing process for a
given work order

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | 34
Holistic Approach to ML/AI
Provided Key elements for AI enablement
High-performance compute and storage
infrastructure optimized for ML
Data-management infrastructure required for
ingesting large volumes of data, data cleansing
and normalization, and data enrichment.
Embedding AI and ML capabilities within its own
business and IT services, using the same and
optimized AI technology stack
Comprehensive environment for rapid
development of advanced ML models
Algorithm
Catalog
Collaboration
Artificial Intelligence Services API
Machine Learning
Frameworks
Auto Model
Selection
Model Dev Tools
Data Analysis
Machine
Learning
Development
Environment
Enterprise
Data Lake
2nd Party
Data
3rd Party
Data
Security
Data
Integration
Data
Enrichment
Data
Preparation
Data API
Data
Management
for ML/AI
GPUs
High Performance Network
High Performance
Object Store
Low Cost
Archive Storage
Infrastructure
for ML/AI
Recommendations, Insights, and Actionable Events
Embedded
ML/AI
Business Apps -
ERP, SCM, CX, HCM
IT Operations –
DB, Security and
Management
Others –
Conversational Chabot
AI-Assisted Analytic
HPC ready infrastructure

Algorithm
Catalog
Collaboration
Artificial Intelligence Services API
Machine Learning
Frameworks
Auto Model
Selection
Model Dev Tools
Data Analysis
Machine
Learning
Development
Environment
Enterprise
Data Lake
2nd Party
Data
3rd Party
Data
Security
Data
Integration
Data
Enrichment
Data
Preparation
Data API
Data
Management
for AI
GPUs
High Performance Network
High Performance
Object Store
Low Cost
Archive Storage
Infrastructure
for AI
Recommendations, Insights, and Actionable Events
Embedded
AI
Business Apps -
ERP, SCM, CX, HCM
IT Operations –
DB, Security and
Management
Others –
Conversational Chabot
AI-Assisted Analytic
HPC ready infrastructure
Oracle Cloud Infrastructure
X7 Compute – HPC Ready
✓ Latest Skylake processors
✓ NVMe SSDs
✓ 50Gbe network
35
Holistic Solution Blueprint
Provided Key elements for AI enablement
High-performance compute and storage
infrastructure optimized for ML
Data-management infrastructure required for
ingesting large volumes of data, data cleansing
and normalization, and data enrichment.
Oracle embeds AI and ML capabilities within its
own business and IT services, using the same
and optimized AI technology stack
Comprehensive environment for rapid
development of advanced ML models
GPU
Cloud
Big Data Cloud DB Cloud
Oracle Analytics Cloud
AI Platform Cloud *Advanced
AnalyticsORAAH
Oracle
Data Management Solutions
CX, ERP, SCM, HCM Cloud
Adaptive Intelligent Apps
IoT
Apps
Cloud
Mobile CS
Intelligent
Bots CS
Management
& Security
Cloud
Autonomous
DWH Cloud *
DaaS
Solutions
(3rd Party
Data)
* Coming SoonLast Update: 18-Jan-2018

https://oracle.github.io/learning-library/workshops/journey3-data-science/?page=README.md
36

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