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Azure PaaSand SaaSMicrosoft’s two approaches to building IoT solutions

Hector Garcia TelladoProgram Manager Lead,

Azure IoT Suite

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Customers using IoT today

Microsoft SaaS IoT offering

Microsoft PaaS IoT offerings

IoT Analytics – What's new!

Agenda

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Customers usingIoT today

#IoTinActionMS

Improving processes, efficiency, and human decision making

with predictive maintenance and tool-level data

Predictive maintenance brings Sandvik to the cutting edge of digital manufacturing

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Norwegian developers at Kongsberg Maritime map unpredictable harbor floor with IoT Hub

Increasing ROI by determining optimal shipping

loads & improving navigation safety

“I had no previous experience with Microsoft. I knew almost

nothing about Azure, the cloud, or IoT. It only took a day or

two to get into it, after which it wasn’t that hard.”

Terje Nilsen, Manager of Disruptive Technology

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How are customersand partners implementing solutions?

Options to deploy cloud IoT solutions

Microsoft IoT CentralQuickly create solutions in

a managed environment

Azure IoT SuiteCustomize to your needs with full control

Remote Monitoring | Predictive Maintence | Connected Factory

SaaSAzure IoT Hub

Azure Stream Analytics

Azure Time Series Insights

Azure Machine Learning

Azure Logic Apps

MorePaaS

No cloud development

expertise required

Fully hosted and

managed by Microsoft

Risk-free trial with

simplified pricing

Reducing the complexity of IoT through managed services

Microsoft IoT Central

Microsoft IoT Central

Microsoft

IoT Central

Risk-free trial with simplified pricing

Analytics, dashboards and visualization

User roles and permissions

Monitoring rules and triggered actions

Device connectivity and management

Time-series Insights

Analytics & dashboards

Device management

Alerts and actions

Template Management

Rules Workflows

Device settings

Product Modeler

Builder Operator

Azure IoT Suite

Combining IoT Services Into An Extensible IoT Solution

Easily build a PoC

Your solution,

in minutes

Customize, extend

and scale

Azure IoT Suite Preconfigured Solutions Features

PaaS

Azure

IoT Suite

Data Ingestion and Command & Control

Stream Processing & Predictive Analytics

Workflow Automation and Integration

Dashboards and Visualization

.NET & Java

Open-sourced, microservices-based

architecture

• Device management, dashboards, commands

• Rules and actions, backend integration

• Add your devices and begin tailor to your needs

Partners accelerate time to value

Start quickly for

common IoT scenarios

• Customize to your assets and rules

• Highly visual for your real-time operational data

• Integrate with back-end systems

Finish with your IoT

application

Preconfigured Solutions Types

New version! New

Preconfigured Solutions Microservices Architecture

Remote Monitoring

Components of a preconfigured solution

Microservices

VM

Devices

Back end

systems and

processes

Cosmos DB

Web App

Logic

AppsIoT Hub

Simulator

Active

Directory

Orchestrator

Microservices

VM

Microservices

VM

Microservices

VM

Remote monitoring | Predictive maintenance | Connected factory | Device Simulation

Azure ML

Azure IoT Edge

Enabling the Intelligent Edge to achieve more

Configure, update and monitor from the cloud

Compatible with popular operating systems

Code symmetry between cloud and edge for easy

development and testing

Secure solution from chipset to cloud

Build once, deploy anywhere

Seamless deployment of AI and

advanced analytics

Azure IoT Edge

Azure

IoT

Devi

ces

Azure IoT Edge Runtime

Azure IoT Edge Architecture

Bridging cloud and devices to provide a cohesive end to end IoT solution

Security Multiplexing Store and forward

Modules (Container)

Managing leaf

devices

Azure Machine

Learning

Cognitive

ServicesAzure Functions Custom Code

Azure Stream

Analytics

In summary: great options for IoT

Microsoft IoT Central in Action: monitoring a device in minutes!

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What's new in IoT Analytics?

Krishna MamidipakaSenior Program Manager,

Azure Big Data

#IoTinActionMS

#IoTinActionMSSource: IDC Digital universe study

Companies that invest in IoT & data analytics

operating margin (18% vs. 10%)technology spend of revenue

Sources: Keystone Research

Unlocking Insights with Real-time analytics

Insights are Perishable

Window of opportunity for insights to be actionable

Time to Insight is Critical

Reducing decision latency can unlock business value

Query data still while it is still in motion

Can’t wait for data to get to rest before running computation

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Mission critical

reliability

Lowest

TCO

Fully

managed

Ease of getting

started

Programmer

Productivity

Declarative SQL

like language

Source/sink

integrations

No cluster

provisioning

Pay as

you goEnterprise

grade SLA

Azure Stream Analytics

IoT Hubs

Archiving for long term storage/ batch analytics

Real-time dashboard

Stream Analytics

Automation to kick-off workflows

Machine LearningReference Data

Event Hubs

Blobs

Devices & Gateways

Presentation & Action

Storage &Batch Analysis

StreamAnalytics

Event Queuing & Stream Ingestion

Event production

Applications

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Making buildings smarter

Benefits ▪ Greener Buildings ▪ Comfortable occupants

“The queries we need to run are quite complicated.... We are able to do this much quicker with Azure Stream Analytics, and with very low overhead.”

- Arvind Shetty, Technology Specialist

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Enabling better business outcomes

“Plant equipment heat and vibration readings are passed along to asset management teams to ensure our equipment is being maintained correctly. Production output can be tracked and provided to our regulator to ensure compliance, and our commercial teams use this telemetry for billing purposes.”

- Kent Weare, Lead Architect

Benefits ▪ Lower equipment failures and downtime ▪ Secure infrastructure ▪ Lower operational costs

Inline Anomaly Detection

Pre-trained ML model

Easily called within our SQL-like query language

Can configure the size of the history window, used to compute martingale values over the look-back history

Simple usage to detect anomalies over one hour of time series data

select id, val, ANOMALYDETECTION(val) OVER(LIMIT DURATION(hour, 1)) FROM input

Usage with partitioning

select id, val, ANOMALYDETECTION(val) OVER(PARTITION BY id LIMIT DURATION(hour, 1)) FROM input

Usage with partitioning and "when"

select id, val, ANOMALYDETECTION(val) OVER(PARTITION BY id LIMIT DURATION(hour, 1) WHEN id != 2) FROM input

Usage showing the extraction of scores:

select id, val FROM input WHERE (GetRecordPropertyValue(ANOMALYDETECTION(val) OVER(LIMIT DURATION(hour, 1)), 'BiLevelChangeScore')) < -1.0

Azure IoT Edge

Azure

IoT

Devi

ces

Azure IoT Edge Runtime

Azure IoT Edge ArchitectureBridging cloud and devices to provide a cohesive end to end IoT solution

Security Multiplexing Store and forward

Modules (Container)

Managing leaf

devices

Azure Machine

Learning

Cognitive

ServicesAzure Functions Custom Code

Azure Stream

Analytics

Analytics closer to devices is key for many IoT scenarios

Ultra low-latency needs

Intermittent connectivity

Bandwidth economics

Compliance requirements

Same language for both

Cloud and Edge “jobs”

Azure Time Series Insights

Oil & Gas

Manufacturing

Smart Energy

Time-series data heavy apps

Smart BuildingInteractive

Analytics

Indexing and

Scalable storage

Visualization

and APIs

Fully-integrated

time series data pipeline

Data parsing and

metadata enrichment

Power & Utility

Demo

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Thank you

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