Machine Learning Estonia Meetup, Applications of Data Science, 11.10.2016

1. Knowledge Discovery in Production
André Karpištšenko

3. Knowledge Discovery
Requires Automation
Growth of information and devices per knowledge worker
1. Digital universe x3.8 in size in 2020. Focus on the highest-value subset.*
2. 26.3B devices in 2020, up +61% from 2015 with x2.7 IP traffic increase.**
3. 700M knowledge workers***, automation worth $5.2T to $6.7T****
* IDC, Apr 2014
** Cisco, Jun 2016
*** Teleport.org, Jun 2016
**** McKinsey, Jun 2016

4. Core Dataflow
Model Engine
Preprocessing Dataflow
System Composition:
Networked Intelligence
Mature
Nascent
Emerging
networked.ai
Infrastructure, Data & IoT Platforms, Advanced Analytics Platforms
Input
Data
Info
Merger
Data Curator Preparer & Explorer
Base Library
SelectorExecutor
Self-improvementInterpreter
Output Interfaces Core Human Interfaces
Knowledge
Manager
Knowledge
Manager

5. Predictive Modeling Flow Example
DashOpt
Feature
Engineering
Raw
Data
Raw
Features
Labels
Feature
Integration
Features
with Labels
Data
Partitioning
Training
Data
Validation
Data
Testing
Data
Model Training
Evaluate for
model selection
Compute offline
evaluation metrics
Best model
Offline scoring
and indexing
Online/offline
systems
Online A/B test
Label
preparation
Log data
Scoring
features
Raw features
Feature
integrationModel
Performance
Test Results

6. Applications in Production
Electronics Manufacturing Biotechnology
Process time reduction
Predictive maintenance Quality improvement
Yield increase

7. Product Preview

8. Preprocessing data for manufacturing
analytics is complex and time consuming.
Custom built preprocessing
solutions are used to gather data
in electronics manufacturing.
The problem
How do people
solve it today

9. Product Scope
Data-driven electronics manufacturing
enabling understanding and prediction
• Heavy machinery
• Automotive
• Consumer Devices & Networks
• Drives
• PLC

10. Product for Pilot Factories

11. Product Solution
• Hybrid SaaS factory subscriptions and applications via open marketplace
• Real-time data streams from the field and factories for R&D and production
Electronics Factories
End Products
IoT Platforms Cloud Services

12. Delivering Business Value
Enabled metrics data
Increased engagement 2x
Enhanced usability of MES
Increased productivity
Test time reduction
270k-290kEUR/plant
Reducing risk through higher quality data and
improving business with data preprocessing

13. Industrial Analytics Example:
Bosch Competition, I
4 product lines
52 stations
Every feature has timestamp
Data rows
Parts of mechanical components
# (training data) – 1 183 747
# (test data) – 1 183 748
Data columns
Anonymized features of stations
Numeric – 970
Categorical – 2 141
Bosch has to ensure that the recipes for the production of its
advanced mechanical components are of the highest quality
and safety standards. Part of doing so is closely monitoring its
parts as they progress through the manufacturing processes.
https://www.kaggle.com/

14. (Dis%nct)pa,erns)of)missing)values)of)all)sta%ons)))
Utilization of stations
Industrial Analytics Example:
Bosch Competition, II
ProductFamilies

15. https://sites.google.com/site/iotminingtutorial/
IoT Data Streams Mining
• Continuous data, dynamic models, distributed, few seconds

16. Streams Mining: Actors Model
Data processing pipeline Distributed processing
Kappa Architecture
https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102

17. DashOpt: Data Science Intelligence

18. Real-Time Predictive Flow
ML & Simulation
Platforms
IoT Platforms
Preprocessed Data
IoT Data
Earth Data
Manufacturing
Data
Predictive Models
Decision Tree SVM
Neural Network Random Forest
Data
Science
Intelligence

19. Outlier Detection
• Single point anomaly detection: likelihood over distribution
• Finding anomalous groups: divergence estimation
• Methods: percentage change, T-test, Chi-square test, Generalized ESD (Extreme
Studentized Deviate) test, Seasonal Hybrid ESD, etc.
• Goal: move from detection to automated response

20. Outlier Detection in Practice
• Too many detections of too little value
• Use methods for thresholds
• Breakout detection and Concept Drift
• For changing distributions move baselines over time
• Risk of overfitting to known anomalies, not finding unknown anomalies

21. Bayesian aka Active Optimization
• Examples: Design of Experiments, hyper-parameters of supervised
learning, algorithms tested with simulations
f is an unknown expensive black-box function with the goal to
approximately optimize f with as few experiments as possible
• No free lunch theorem
• Other bio-inspired
algorithms for optimization
exploitation and
exploration: neural
networks, genetic algorithms,
swarm intelligence, ant
colony optimisation, etc.

22. Bayesian Optimization in Practice
• SigOpt experience: 20 dimensions, above human capacity.
• Uber ATC experience: scaling active optimization to high
dimensions default works reliably for 5-7 dim.
• Variables are added during optimization.
• Choose fidelity using heuristics.

23. DashOpt: Data Science Intelligence
US Patent pending

24. Extensive data bases of DNA sequences,
metabolism of cells and components – enzymes
etc., high-throughput experimental omics-
methods
Software environment for in silico ab initio
design of cells, and in silico testing
(predictive modeling) of the cell designs in
manufacturing processes
Current State in Biotech
Already available Future state

25. Thinking about Value from Data Science