Azure PaaS and SaaS Microsoft s two approaches to building IoT solutions Hector Garcia Tellado Program Manager Lead, Azure IoT Suite #IoTinActionMS #IoTinActionMS
Agenda Customers using IoT today Microsoft SaaS IoT offering Microsoft PaaS IoT offerings IoT Analytics What's new!
Customers using IoT today #IoTinActionMS
Predictive maintenance brings Sandvik to the cutting edge of digital manufacturing Improving processes, efficiency, and human decision making with predictive maintenance and tool-level data #IoTinActionMS
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 #IoTinActionMS
How are customers and partners implementing solutions? #IoTinActionMS
Options to deploy cloud IoT solutions Azure IoT Suite Customize to your needs with full control Remote Monitoring Predictive Maintence Connected Factory Microsoft IoT Central Quickly create solutions in a managed environment PaaS Azure IoT Hub Azure Stream Analytics Azure Time Series Insights Azure Machine Learning Azure Logic Apps More SaaS
Microsoft IoT Central Reducing the complexity of IoT through managed services Fully hosted and managed by Microsoft No cloud development expertise required Risk-free trial with simplified pricing
Microsoft IoT Central Device connectivity and management Monitoring rules and triggered actions Analytics, dashboards and visualization Microsoft IoT Central User roles and permissions Risk-free trial with simplified pricing
Builder Operator Product Modeler Device settings Template Management Rules Workflows Device management Analytics & dashboards Time-series Insights Alerts and actions
Azure IoT Suite Combining IoT Services Into An Extensible IoT Solution Your solution, in minutes Easily build a PoC Customize, extend and scale
Azure IoT Suite Preconfigured Solutions Features Data Ingestion and Command & Control Stream Processing & Predictive Analytics Workflow Automation and Integration Dashboards and Visualization Azure IoT Suite Open-sourced, microservices-based architecture PaaS.NET & Java
Partners accelerate time to value Start quickly for common IoT scenarios Finish with your IoT application Device management, dashboards, commands Rules and actions, backend integration Add your devices and begin tailor to your needs Customize to your assets and rules Highly visual for your real-time operational data Integrate with back-end systems
Preconfigured Solutions Types New version! New
Preconfigured Solutions Microservices Architecture Remote Monitoring
Components of a preconfigured solution Remote monitoring Predictive maintenance Connected factory Device Simulation Devices Web App Active Directory Simulator IoT Hub Cosmos DB Microservices Microservices Microservices Logic Apps Back end systems and processes Orchestrator VM VM VM VM Azure ML
Azure IoT Edge Enabling the Intelligent Edge to achieve more Seamless deployment of AI and advanced analytics Build once, deploy anywhere Configure, update and monitor from the cloud Code symmetry between cloud and edge for easy development and testing Compatible with popular operating systems Secure solution from chipset to cloud
Devices Azure IoT Edge Architecture Bridging cloud and devices to provide a cohesive end to end IoT solution Azure IoT Edge Modules (Container) Azure Machine Learning Azure Stream Analytics Azure Functions Cognitive Services Custom Code Azure IoT Edge Runtime Security Multiplexing Store and forward Managing leaf devices Azure IoT
In summary: great options for IoT
Microsoft IoT Central in Action: monitoring a device in minutes!
What's new in IoT Analytics? Krishna Mamidipaka Senior Program Manager, Azure Big Data #IoTinActionMS #IoTinActionMS
Source: IDC Digital universe study #IoTinActionMS
Companies that invest in IoT & data analytics technology spend of revenue operating margin (18% vs. 10%) 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
Azure Stream Analytics Ease of getting started Programmer Productivity Fully managed Lowest TCO Mission critical reliability Declarative SQL like language Source/sink integrations No cluster provisioning Pay as you go Enterprise grade SLA #IoTinActionMS
Applications Devices & Gateways Event Hubs Stream Analytics Archiving for long term storage/ batch analytics IoT Hubs Blobs Real-time dashboard Reference Data Machine Learning Automation to kick-off workflows Event production Event Queuing & Stream Ingestion Stream Analytics Storage & Batch Analysis Presentation & Action
Making buildings smarter 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 #IoTinActionMS Benefits Greener Buildings Comfortable occupants
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 #IoTinActionMS 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
Devices Azure IoT Edge Architecture Bridging cloud and devices to provide a cohesive end to end IoT solution Azure IoT Edge Modules (Container) Azure Machine Learning Azure Stream Analytics Azure Functions Azure IoT Edge Runtime Cognitive Services Security Multiplexing Store and forward Custom Code Managing leaf devices Azure IoT
Analytics closer to devices is key for many IoT scenarios Same language for both Cloud and Edge jobs Ultra low-latency needs Intermittent connectivity Bandwidth economics Compliance requirements
Azure Time Series Insights Manufacturing Fully-integrated time series data pipeline Data parsing and metadata enrichment Oil & Gas Power & Utility Indexing and Scalable storage Interactive Analytics Smart Building Smart Energy Visualization and APIs Time-series data heavy apps
Demo
Thank you #IoTinActionMS #IoTinActionMS