Knowledge Discovery in Production

Knowledge Discovery in Production
Knowledge Discovery in Production André Karpištšenko
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
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
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
Applications in Production Electronics Manufacturing Biotechnology Process time reduction Predictive maintenance Quality improvement Yield increase
Product Preview
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
Product Scope Data-driven electronics manufacturing enabling understanding and prediction • Heavy machinery • Automotive • Consumer Devices & Networks • Drives • PLC
Product for Pilot Factories
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
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
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/
(Dis%nct)pa,erns)of)missing)values)of)all)sta%ons))) Utilization of stations Industrial Analytics Example: Bosch Competition, II ProductFamilies
https://sites.google.com/site/iotminingtutorial/ IoT Data Streams Mining • Continuous data, dynamic models, distributed, few seconds
Streams Mining: Actors Model Data processing pipeline Distributed processing Kappa Architecture https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102
DashOpt: Data Science Intelligence
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
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
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
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.
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.
DashOpt: Data Science Intelligence US Patent pending
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
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Knowledge Discovery in Production

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