predictive analytics: why (i)iot is different
TRANSCRIPT
Predictive Analytics : Why (I)IoT is different
Or is it?
Venu Vasudevan, PhD
Consultant IoT | Big Data Adjunct Professor, ECE, Rice U.
[email protected] @venuv62
Me
Intrapreneur. balanced diet of IoT & predictive analytics
๏ IIoT for asset management. Key contributions to Zigbee
๏ Shazam for IoT - IoT accessory Home/Auto
๏ Iridium predictive fault management
1Mill measurands/sec. then Satellite ~ now thermostat
๏ Predictive video analytics (acquired by WatchWith)
๏ Mobile Content Recommender (shipped in China mkt)
Agenda
• What is unique about the I in IIoT
• Why does it need predictive analytics
• Predictive Analytics + IIoT - more than sum of parts? Now & Next
IIoT Market Potential
Large addressable marketLow(er) business friction
technology creators are also customers
Business Focus : From Reliability to Optimization reactive/descriptive to predictive
over-doing processes expensive
under-doing processes
catastrophic
rightsizing a predictive process
(time | business context)
e.g. too much ‘routine’ maintenance. lightly used
equipment
e.g. not enough maintenance.
high risk equipment
Predictive Analytics : IoT Challenge
good enough predictions with incomplete, untidy data
Source. Par stream IoT survey
IIoT+Predictive:more than sum of parts?
IoT
Predictive Analytics
retrospective descriptive prescriptivepredictive
How does ‘industrial’ influence big data architecture
How does service architecture influence prediction
Deeper Dive
collect
learn
act
sense
How does IIoT influence big data architecture
How does service architecture influence prediction
store.query.
analyze.predict
human. automated.
Sensing Data Challenge
Option1. Faster data to decisioning Fatter, faster pipes
Continuous flow
Option 2. Intelligent Edge Move decisioning to data
Periodic update
sense
getting data and decisioning together
Towards Edge Architectures
GE Blog - Edge: A Door to the Data Kingdom
Devil in detailsedge standardization
predictive intelligence distributed?
Slow lakes to fast streams
• Now. Transition from data lakes to data streams
‣ 30-100x speed up : streams over lakes
‣ needed to deal with real-time IIoT traffic
‣ lambda architectures balance prediction speed and accuracy
• But ..
untidydata
firehose
cleananalytics
fast & good
slower & much better
Lambdaapproach
collect
Hadoop
Spark
Edge Filtering : Slimming diet for fat streams
Application
Database
More than 20 billion records returned
Query Search Results 40 records found
4 billion records
4 billion records
4 billion records
4 billion records
4 billion records
Application
Query Search Results 40 records found
ParStream
ParStream Geo-Distributed Server
7 records
18 records
5 records
12 records
8 records
ParStream ParStream ParStream ParStream
Opportunity : Learning at Massive Scale
• Machine-learning-as-a-service frameworks offer rich set of algorithms, solution templates - immediate impact in:
• problems with established procedures
• and clean data
Source. Cortana Intelligence Gallery
learn
Challenge : Data Wrinkles
• Machine-learning-as-a-service frameworks offer rich set of algorithms
• Limiting factor is the data-insight gap
data maturity
insi
ght
insight aspiration
data reality
variabilityvolume veracity
Stanford study. Electricity demand forecasting. Deep learning 3x better than ‘classic’ m/c learning
IIoT vs Consumer Web : Same problems, different wrinkles
• Machine-learning-as-a-service frameworks offer rich set of algorithms
• Limiting factor is the insight-data gap
• Reasons for insight-data gap distinct in IIoT over consumer
consumer IIoT
capture hard easy
sanitization medium hard
modeling/integration easy hard
Two-Tier Machine Learning for IIoT
• Machine-learning-as-a-service frameworks offer rich set of algorithms
• Limiting factor is the insight-data gap
• Reasons for gap distinct in IIoT over other domains
• Solution - Machine Learning for IoT data wrangling
Conclusion
Present : Cloudy
• embrace. leverage cutting edge cloud and ML services
• extend. adapt to IIoT business processes
Future : Edgy
• hyper decentralized intelligence and data
• systems that understand ‘normal’ and ‘deviation’
• predictive systems that have both response velocity and depth of insight
