Machine Learning - Eine Challenge für Architekten

© Hitachi Vantara Corporation 2018© Hitachi Vantara Corporation 2018.
Eine Challenge für Architekten
#DOAG2018, Nürnberg, 20. November 2018
Machine Learning
Harald Erb
Solutions Engineer, EMEA Central
Data Analytics & IoT

© Hitachi Vantara Corporation 2018
OT
107+
YEARS
BUSINESS
INDUSTRIAL
CONSUMER
CITY
IT
58+
YEARS
COMMUNICATIONS
BIG DATA
ANALYTICS
ARTIFICIAL
INTELLIGENCE
CLOUD
IT SYSTEMS
IoT
INSIGHT
Hitachi
Hitachi Vantara: What? And Why?

Analytics
Artificial
Intelligence
Machine
Learning
Stream
Analytics
Batch
Analytics
Data Management
Data
Orchestration
Data
Engineering
Data
Blending
Data
Collection
Asset
Asset
Management
Asset
Avatars
Data
Stores
Business
Connectors
APIs,
App-Enabling Studio
Alerts,
Notifications
Dashboards,
UI, UX
Maintenance Insights Manufacturing Insights Video Insights
Manufacturing Utilities Logistics Oil & Gas Mining Financial Services Other
Co-Creation
Services
Professional
Services
Edge
Asset
Integration
Device
Control
Data Caching,
Filtering
◼ Industry solutions (Hitachi BUs, ISVs, SIs) ◼ IoT applications ◼ IoT platform services ◼ Edge-to-cloud infrastructure
Foundry
Edge controllers,
appliances
Converged and
hyperconverged
Block-, file- and
object storage
Pentaho
IoT Solution Portfolio of Hitachi Vantara

public class AgendaForThisTalk {
public String Topic1 = "Process, Architecture and 1 Example";
public String Topic2 = "1 Dataset, 4 different Tools";
public String Topic3 = "From Prototype to Production";
}

Process, Architecture and 1 Example_

Data Warehouse & Analytics back in 1998
z
Data
Discovery
z

Architecting for Analytics & Machine Learning today
Source: Carlton E. Sapp: “Preparing and Architecting for Machine Learning”, Gartner, 2017

Analytic Dashboard Example
Source: Carlton E. Sapp: “Preparing and Architecting for Machine Learning”, Gartner, 2017

Überschrift
Source: XXXXXX
End-to-end Fleet Management Solution
• Combine Sensor Data with Contextual Information
• Overcome scarce availability of capable technicians
• Lower costs, reduced customer downtimes_
Fleet Optimization

Truck Leasing Company
Issues: Trucks have become more and more technology based, availability of capable technicians
is scarce, and managing large number of maintenance centers is expensive.
Business Objectives: Better Fleet Management - lowering costs, improving efficiency of
maintenance to reduce customer downtime
Strategic Goals: Need to take competitive advantage of truck data. 40-50,000 trucks purchased
per year – more data than any truck OEM. Gain a competitive edge through lowered costs &
predictive technology (automation in repairs & diagnostics).

Vehicle Sensor Data
Store models and sensor data
Asset ModelSensor DataUtility Vehicle Asset
Air Pressure
Axle Vibration
Lights
Load Weight
Movement
Temperature
Sensor
Data
Journey
Stream
Blend
Infer
Sense
Inspect
Embed & Integrate
Store

Adding Context to Sensor Data
IoT Data Refinery
Contextual DataSensor Data
Sensor
Data
Journey
Stream
Blend
Infer
Sense
Inspect
Embed & Integrate
Store
Vehicle Location
• GPS
• Lat / Long
• Mapping
• Movement
Vehicle Profile
• Make
• Model
• Mileage
Operational
Systems
• Maintenance
History
• Maintenance
Schedule
• Service Centers
• Parts Ordering
• Parts Inventory
Business
Outcomes
• Real-Time Fleet Status and Health
• Repair Recommendations
• Optimized Maintenance Scheduling
• Automated Parts Ordering

Fleet Management Dashboard: Situation Overview
Source: XXXXXX

Fleet Management Dashboard: Contextual View
Source: XXXXXX

Data Science – Personas & Process Model
Source: K. Bollhöfer, Chief Data Scientist, *um
Cross-functional
team

Architecture Challenge
Source: D. Sculley, et al.: “Hidden technical debt in Machine learning systems”, 2015

1 Dataset, 4 different Tools_

1 Dataset, 4 Tools
Dataset:
California
House Prices
github.com/ageron/handson-ml/tree/master/datasets/housing
Tools:
Jupyter Notebook
• End-to-end ML Projects
• ML with preferred programing
language like Python, R, Julia
• Live-Code embedded in Markup
Document
Oracle Data Visualization
• For data exploration
• ML to explain dependencies in
dataset
• 1-click analytical functions and
model training possible
H2O Flow
• End-to-end ML Platform with own
compute engine, AutoML,…
• Notebook UI, H2O algorithms
accessible from Python and R
• Java-Export of trained Models
Pentaho Data Integration
• Embedding ML Code in ETL
dataflows
• ML Orchestration: Model training
and management
• Plugin Machine Intelligence
Free trial versions
available for all tools!

Machine Learning Coding with Jupyter Notebook & Python
Start here: jupyter.org

Überschrift
Source: XXXXXX

Machine Learning with Jupyter Notebook & Python
ML Process support: End-to-end, focus on experimentation not on production ML
Personas: Data Scientists
Useful: • Notebooks allow reproduceable ML from data ingestion to model
evaluation
• Multiple programming languages, access to latest ML frameworks via
Python interface
• Sophisticated visualizations
Architecture &
Development
related:
• Trained ML models can be serialized/saved, i.e. via Pickle, Joblib
• Models can be published as REST API endpoints, ie. via Flask
• Notebooks stored as JSON files → Code versioning / merge not allways
easy, less comfort compared to other IDE‘s (no syntax highlighting,
no key word completion)
• Large-scale ML possible, i.e. via Apache Spark MLlib; Cluster
deployments are better done separately

Dataset Exploration with Oracle Data Visualization
Start here: www.oracle.com/technetwork/middleware/oracle-data-visualization

Überschrift

Dataset Exploration with Oracle Data Visualization
ML Process support: Data Understanding phase, Results Presentation & Story Telling
Personas: Business Analysts, Data Scientists
Useful: • Highly interactive Charts and Filters and intuitive analysis support
(i.e. pattern brushing)
• Supporting functions to highlight and explain attribute/variable
dependencies
• Formular editor, advanced (ML) functions and Model training and
scoring available for experimentation (experienced Users only)
Architecture &
Development
related:
• Use Oracle Analytics Cloud for better collaboration; can be combined
with Data Visualization Desktop
• Dataset preparation functionality is improving over time, but will not
replace existing ETL platforms (Scalability, Job-Management)
• Good for rapid prototyping, but limited reusability of results
(curated datasets, ML Models)

Machine Learning Orchestration and more with Pentaho
Start here: community.hitachivantara.com/docs/DOC-1009931-downloads
Blog: community.hitachivantara.com/community/products-and-
solutions/pentaho/blog/2018/10/16/deep-learning-coming-to-pentaho

Bring Your Own (ML) Code

ETL-Tools + Python/R – A Door Opener for Deep Learning?

Embedding ML Algorithms into Data Pipelines
Source: XXXXXX

„Model Zoo“ managed by your Data Integration Solution

Machine Learning within Data Integration Platforms
ML Process support: Data Preparation phase, Operationalize ML, Experimentation?
Personas: Data Engineers, Data Scientists (supporting)
Useful: • DI platforms provide advanced data preparation and automation
features for effective creation of datasets
• Intuitive UI, Drag & drop instead of coding
• Skilled DI team already in place
Architecture &
Development
related:
• DI platforms are optimized to utilize full computing power for ETL/ELT,
but not for ML Tasks (i.e. parallel execution might need extra effort)
• When using R / Python interfaces: ensure to collect status infos +
performance metrics of your ML model execution within DI logging
• „ML toolbox“ of DI solutions is often not complete: i.e. script-based
work arounds needed for imputation of missing values, working with
latest algorithms, model management
• Limited and not intuitive data exploration features, i.e. visualisations

End-to-end Machine Learning for everyone with H20 (Flow)
Start here: www.h2o.ai/download
Accessing a 2 node H2O cluster in a R environment

End-to-end Machine Learning for everyone with H20 Flow
ML Process support: End-to-end, focus is on Model training/evaluation, Feature Engineering
Personas: Data Scientists, ambitious Business Analysts(?)
Useful: • H2O Flow: intuitive Notebook-style UI + user guidance
• Use the programing language you already know like R, Python
• AutoML can be used for automating the ML workflow, including training
and tuning of many models within a user-specified time limit
Architecture &
Development
related:
• Takes advantage of the computing power of distributed systems and in-
memory computing to accelerate machine learning
• Works on existing big data infrastructure, on bare metal or on top of
existing Hadoop or Spark clusters; can ingest data from HDFS, Spark, S3
• Model deployment into production with Java (POJO) and binary formats
(MOJO), Hive UDF, or as API endpoint
• H2O Flow: Limited Data Exploration (no visual only in combination with
R/Python

Another tool decision: Which ML Framework to choose?
Source: Dr. D. James: „Entscheidungsmatrix „Machine Learning“, it-novum.com

From Prototype to Production_

From Exploratory Data Science to Production Workflows
Line of
Governance
• Commercial exploitation
• Integration to operations
• Non-functional requirements
• Standardisation & governance
Model
• Unbounded discovery
• Self-Service sandbox
• Wide toolset / IDE’s
• Agile methods

Model deployment
Source: Sergei Izrailev: Design Patterns for Machine Learning in Production, 2017
• Data transformations must be the
same in training and scoring
• Interface between building &
scoring:
− In-memory - model is never
persisted, train then score
→ single & applications
− Data only, i.e. PMML, etc
→ code is independent
− Serialized objects - Pickle, R,
Spark, custom → reuse code
− Code + Data – i.e., H2O’s POJO
→ code is generated
Detailed article in
“Java aktuell” 06/2018

Überschrift
Source: XXXXXX
H2O POJO (Plain Old Java Object):
• ML Model implemented through
Java classes
• has dependencies with H2O
specific classes

Gradient Boosting Machines (GBM)
• A family of powerful machine-
learning techniques for regression
and classification problems
• GBM’s produce a prediction model
in the form of an ensemble of weak
prediction models, typically
decision trees

From Prototype to Machine Learning in Production
Source: Sergei Izrailev: Design Patterns for Machine Learning in Production, 2017

Applying ML Model for Scoring of new (unlabeled) Data
Source: K. Wähner: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka
• Add ML model =
Java application to
Apache Kafka
stream processing
application to
apply it on new
incoming events
• Spark Streaming
also allows ML
model serving via
mini batches and
makes use of in-
memory
processing and
load distribution

ML Model as API endpoint: Object detection example
Source: A. Rosebrock: “A scalable Keras + deep learning REST API”, pyimagesearch Blog
• “API first” approach instead of rewriting
code to replicate ML model in a
programming language supported by the
enterprise IT
• Web APIs have made it easy for cross-
language applications to work well. If a
developer needs a Model to create a ML
powered web application, they would
just need to get the URL Endpoint from
where the API is being served
• Webservice development frameworks like
Flask allow prototyping in Python
• For production environments, an
additional web server and a messaging
should be considered
Keras: a simple, modular, and extensible Deep Learning library, written in Python and
designed to enable fast experimentation with deep neural networks
Redis (Remote Dictionary Server): implements a distributed, in-memory key-value
database with optional durability

Object Detection – Use Case
PicSure AI Platform für Versichrungslösungen

Takeaway
• Find the right problem
• Define constraints
• Design components and interfaces
• Take into account organizational constraints
• Production can’t be an afterthought
• The process is a lot of work, but it’s not
rocket science
Processing huge amounts of data with complex algorithms can a bit too much of
time. Kudos Randall Munroe / XKCD for the original

Machine Learning - Eine Challenge für Architekten

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Machine Learning - Eine Challenge für Architekten