What Is Machine Learning?
Where do we deploy machine learning and what software and cloud services are out there to support it?
What are the trends in deploying these systems and what are the benefits for IT?
Do you have a IoT Machine Learning Case Study in the Cloud?
7. 2. Directed Knowledge
where knowledge created
elsewhere (by a central
authority) will be used to
modify edge behavior
Cloud
1. Observed
Knowledge
which will modify
behavior based on local
learning (context)
Edge
3. Sensor Fusion Knowledge
the combining of sensory data and
data delivery orchestration such
that the resulting information is in
some sense better than would be
possible when these sources were
used individually. See Kalman filter
8.
9. IoT Scenario
Predictive Maintenance in IoT Traditional Maintenance
Goal
Improve production and/or maintenance
efficiency at lowest cost
Ensure scheduled
maintenance has been done
Data
-Data stream (time varying features)
-Multiple data sources
Tasks completed to be done
Tasks
-Failure prediction
-Fault/failure detection & diagnosis,
-Recommendation maintenance actions
-Fault/failure tracking
-Procedure for Diagnosis
10. Develop ML model
(MATLAB)
alongside local
university
Optimise code
Reduce runtime
Build
evaluation
module
Refine model
parameters
Develop
user web
front end
IoT Predictive Maintenance – Qantas Airways
~24,000 sensors
Qantas A380 Fleet
Technical Delays
12
$65M+
per A380
50%
Technical Delays
400-700 Fault/warning
messages/day
have potential for predictive
modelling
Configure model
in AML PM
template
Evaluate & refine
model data &
parameters
Visualize results
in Power BI
Months
/year
Orchestrate data
pipeline in Azure
Data Factory
Source: www.microsoft.com
11. Stay ahead of the curve with Cortana Intelligence Suite
Business
apps
Custom
apps
Sensors
and
devices
People
Automated
systems
Data
Machine Learning
Ecosystem
Cortana Intelligence
Action
Apps
12. The IoT Ecosystem Around ML
Intelligence
Dashboards &
Visualizations
Information
Management
Big Data Stores Machine Learning
and Analytics
CortanaEvent Hubs
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot
Framework
SQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory
Machine
Learning
Data Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
Machine Learning
Ecosystem
18. Good Scope for ML Experiment
Question
is sharp.
Data
measures
what they
care
about.
Data is
connected.
Data is
accurate.
A lot of
data.
The better the raw materials, the better the product.
E.g. Predict
whether
component X will
fail in the next Y
days; clear path
of action with
answer
E.g. Identifiers at
the level they are
predicting
E.g. Will be difficult
to predict failure
accurately with few
examples
E.g. Failures are
really failures,
human labels on
root causes; domain
knowledge
translated into
process
E.g. Machine
information linkable
to usage
information
20. Load The Data: Data Sources
The failure history of a machine
or a component
The repair history
Previous maintenance records,
Components replaced
Maintenance opeators
Performance data collected from
sensors.
FAILURE HISTORY REPAIR HISTORY MACHINECONDITIONS
The features of machine or
components, e.g. production
date, technical specifications.
Environmental features that may
influence a machine’s
performance, e.g. location,
temperature, other interactions.
The attributes of the operator
who uses the machine, e.g. driver.
MACHINE FEATURES OPERATING CONDITIONS OPERATORATTRIBUTES
27. Modelling Techniques
Predict failures within a future
period of time
BINARY CLASSIFICATION
Predict failures with their causes within
a future time period.
Predict remaining useful life within
ranges of future periods
MULTICLASSCLASSIFICATION
Predict remaining useful life, the
amount of time before the next failure
REGRESSION
Identify change in normal
trends to find anomalies
ANOMALYDETECTION
30. Acknowledgements
• We utilized the following publically available data to help us generate realistic data for
the demo shown. We received assistance in creating this solution as a result of this
repository and the donators of the data:
“A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics
Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames
Research Center, Moffett Field, CA.”
• McKinskey Global Institute, The Internet of Things: Mapping the Value beyond the hype
• Microsoft Cortana Gallery Experiments
31. Learn and try yourself!
• Learn from Cortana Analytics Gallery
• Solution package material – deploy by hand to learn here
• Try Cortana Analytics Solution Template – Predictive
Maintenance for Aerospace in private preview
• Try Azure IOT pre-configured solution for Predictive
Maintenance
• Read the Predictive Maintenance Playbook for more details
on how to approach these problems
• Run the Modelling Guide R Notebook for a DS walk-
through
32.
33. • Contact us for 1 free consultation: giuseppe@valueamplify.com
• Twitter: @giuseppeHighTec
• Linkedin: www.linkedin.com/in/giuseppemascarella
Notas do Editor
Check if will prompt music or other services depending on status
Guided menu “Press 1,2,3” vs alexa (did you mean x or Y)
Designing with artificial intelligence
The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism.
The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism. For IoT devices, the interface may be as minimal as a few LEDs and a touchpad—and that kind of minimalism can feel obscure and confusing to users. What’s more, IoT devices often need to operate in concert to create delightful services, such as coordinating the levels of light and sound in a room. This simply increases complexity. Unless we come up with new ideas, the world is about to feel terribly broken.
That’s why interfaces and services increasingly rely on artificial intelligence technologies. Algorithms make sense of contextual data, anticipate user needs, and accept more natural forms of input, like voice commands. Keeping the interface simple means the device has to become more intelligent.
AI isn’t magic—it’s engineering. To develop compelling products, designers and product managers need to understand the constraints and possibilities of AI. They also need to develop new ways of working together so that the resulting products and services feel more… human.
This session looks at how algorithms work, examines what they can and can’t do, and explores case studies and examples of how product teams have combined a deep understanding of people with clever design and smart algorithms to produce truly wonderful products.
Decisions of what data to keep, ignore, and what to forward to a centralized authority will be required. Many of the kinetic devices will be used and application whose action can neither tolerate long latency nor risk the possibility that the connection with the centralized authority (“the cloud”) is not available. Their decisions must be made instantly with local information and knowledge. Most IoT endpoints will be limited in capabilities due to size, cost, and the power requirements and will need companion computing that is either embedded in the larger system or in a companion gateway. These gateways will primarily bridge between the local device communication domains and higher level network domains and will in most cases make behavioral decisions. As the industry matures, these gateways will also be responsible for allowing data to be exchanged between intended devices, and ensuring the information is protected. Network traffic patterns will be significantly impacted as more device-to-endpoint traffic will occur and more machine-to-machine communication will materialize, shifting from today’s patterns. However, these solutions will not be static, and their evolving behavior will need to vary depending on local characteristics, giving rise to more software-defined functions at both the edge and within the datacenter. Further, their numbers will be vast and their operation cannot require human intervention.
Sensory fusion Sensor fusion is a term that covers a number of methods and algorithms, including: Central Limit Theorem, Kalman filter, Bayesian networks, Dempster-Shafer
Example: http://www.camgian.com/ http://www.egburt.com/
Kalman is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe.
You have enabled Media Viewer for all files
Next time you click on a thumbnail on Wikipedia, Media Viewer will be used.
You have disabled Media Viewer
Next time you click on a thumbnail on Wikipedia, you will directly view all file details.
Media Viewer is now disabled
Enable Media Viewer?
Enable this media viewing feature for all files by default.
Learn more
Enable Media ViewerCancel
Disable Media Viewer?
Skip this viewing feature for all files.
You can enable it later through the file details page.
Learn more
Disable Media ViewerCancel
More details
The Kalman filter keeps track of the estimated state of the system and the variance or uncertainty of the estimate. The estimate is updated using a state transition model and measurements. denotes the estimate of the system's state at time step k before the k-th measurement yk has been taken into account; is the corresponding uncertainty
Selected raw features The raw features are those that are included in the original input data. In order to decide which raw features should be included in the training data, both the detailed data field description and domain knowledge is helpful. In this template, all the sensor measurements (s1-s21) are included in the training data. Other raw features get used are: cycle, setting1-setting3.
Aggregate features These features summarize the historical activity of each asset. In the template, two types of aggregate features are created for each of the 21 sensors. The description of these features are shown below.
a1-a21: the moving average of sensor values in the most w recent cycles
sd1-sd21: the standard deviation of sensor values in the most w recent cycles