Introduction to Time Series Analytics with Microsoft Azure
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Improve operations and decision-making by using real-time data insights and interactive analytics to accelerate IoT data use throughout your organization. Discover the webinar here: https://bit.ly/38sMcrP
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Introduction to Time Series Analytics with Microsoft Azure
- 1. Welcome Webinar | Introduction to Time Series Analytics 1
- 2. Azure MVP SamVanhoutte Time traveling in the cloud Time series Analytics with Azure Hi, I am Sam, CTO of Codit 2
- 3. This session 3 Time series characteristics Customer cases and scenarios Azure Time Series Insights & Data Explorer Azure Machine Learning Conclusions
- 5. Intents vs Facts 5 Messages Events Intents Commands Query Job Assignment Update Request Report Notification Measurement Trace Audit Facts
- 6. 4 components of Time Series 7 Seasonality Trend Cyclicity Irregularity
- 7. Multiple time series | Not all time series have easy to detect components | Multiple related time series can vary differently over time | Combination of parameters at a given time can indicate state | Time windows can result in much more relevant findings
- 8. Examples 9 | Stock prices | Weather reports | Electricity demand | Revenue numbers | Temperature readings | Number of passengers | Criminality numbers
- 10. Some use cases 11 Improved outcomes and increased revenue Industrial IoT & Supply Chain Optimization Predictive & preventive maintenance Delivery optimization Real-time anomaly detection Energy planning & trading Sensor stream data Inventory data Production data Transport & Retail data Tuning parameters Manufacturing Improved consumer engagement with machine learning Data-driven stock, inventory, ordering Demand-elasticity Predict inventory positions & distribution Right product, promotion, at right time Shopping history Online activity Demand plans Forecasts Sales history Retail Enhanced customer experience with machine learning Risk, fraud, threat detection Predictive analytics & targeted advertising Card monitoring & fraud detection Decision simulations & forecasting Transaction data Market data Purchasing History Clickstream data Financial Services
- 11. 12 Smart Heating and Ventilation at Duco
- 12. 13 Power and Prediction: Azure IoT Keeps ENGIE Ahead of Issues - and its Market
- 14. Communication & runtime 15 PLCs, Databases, Message Buses, SCADA Systems, MES Systems, ERP Systems Processing IoT Hub & DPS Data integration IoT Edge Publisher Storage Twin File upload Telemetry Device twin Commands Methods MQTT AMQP HTTPS MQTT Lifecycle Provisioning Actions Hot path analytics Cold path analytics Long term storage Applications Digital twin Relations DevOps Monitoring Security Infrastructure Reference architecture Environ- ment Stream Analytics Azure ML Cognitive Event Grid Functions Time Series Insights Azure SQL Database Blob Storage Data Factory Blob Storage Cosmos Db Data Lake Synapse Databricks Azure ML Data explorer ASA Azure ML Time Series I. Logic Apps Functions Devops App Service Power BI Data Share Power Platform App Service Tenants
- 16. Predictive maintenance data set 17 | Public dataset (Nasa Turbo fan) | Damage propagation for aircraft engine | Run-to-failure simulation | Aircraft gas turbines | Dataset contains time series (cycles) for all measurements of 100 different engines https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan
- 17. Architecture 18 IoT Hub Time Series Stream Analytics Logic Apps Alerting Event Grid Detect maintenance needs Logic Apps Alerting ! IoT Edge Machine Learning https://github.com/SamVanhoutte/azure-time-travel
- 18. Canonical Industrial IoT Data pipeline 19
- 19. 20 Time series insights & Azure Data Explorer
- 20. 21 Time Series data Azure offers two services to ingest, process, store and query highly contextualized, time-series-optimized IoT-scale data: Azure Time Series Insights & Azure Data Explorer
- 21. Azure Time Series & Data Explorer 22 Azure Time Series Insights | Built on top of ADX | Very easy to set up | Perfect for exploratory and visualization purposes | Query possibilities through the API Azure Data Explorer | Foundational service for many other Azure services | Extremely powerful | No exploration portal | Queries through KQL | Fully customizable
- 22. 23 Model training with AzureML
- 23. Scenario: Prevent outage of engines 24 Job to be done What are you trying to achieve? Business Impact Benefits How will it used in the processes What actions are linked to decisions Data fuel What data is available? Are data streams available? Is the training data labeled? Definition of success Predict Evaluate Trust What do we want to predict? Classification / estimated value What if the model is wrong? What accuracy do we expect? Evaluation period When do we trust the model? What is needed to call this a success? Risks What risks do we see for the project? Feedbackloop Possibilities to improve & retrain Future scenarios Related scenarios & applications Designed for: demo purposes Designed by: Sam Vanhoutte Date: July 31, 2020 Predict time to failure of engines Stream: 100 engines, 24 sensor values 20.631 labeled records People involved Stakeholders, users, decision makers Users: Operators Time for maintenance Classify for warning Regression of ttf False alerts are better than missed anomalies Accuracy > 90% Model can be used to alert people who can double check When outage of production decreases When false alerts are not happening a lot Avoid downtime Increase reliability Impact of different engines? Finding the right ttf threshold Side / side human validation Integrate alerts with servicedesk system Deploy to the edge
- 24. MLOps process (generalized) 25 Analyze Signals for Retraining Register Model Model Registry Model Telemetry Validate & Deploy Collect Feedback ML Pipeline Publish training pipeline Submit Code for review Experiment Interactively Data Scientist ML Engineer Batch predictions Real time predictions User-facing application Train Model
- 26. 3. Stream Analytics: in the cloud & on the edge 27 Presentation & Action Storage & Batch Analysis Stream Analytics Event Queuing & Stream Ingestion Event production IoT Hubs Applications Archiving for long term storage/ batch analytics Real-time dashboard Stream Analytics Automation to kick-off workflows Machine Learning Reference Data Event Hubs Blobs Devices & Gateways PowerBI
- 27. Takeaways 28 | Ingest data into Time Series Insights | Enable Data exploration, querying and visualization | Extend to Machine Learning, Data Science and Front End applications | Out of the box integration with Data Lake, Power BI, etc Azure offers plenty options for Time Series processing
- 28. Reference case 29 Getting Started | Request your workshop | 2 flavors | IoT | Data / AI | Outcomes | Business case definition & strategy | Requirements | Azure capabilities | Architecture | First proof-of-concept
- 29. Your Feedback is Valuable to Us 30
- 30. Thank you. Let’s connect! 31
Editor's Notes
- https://wall.sli.do/event/mr4f4kug?section=fc9aec73-be4f-4146-b1be-f18b8dcae466
- Seasonality: variations that repeat over periode (shorter periods) Trend : long term variation Cyclical effect: fluctuations around trend (economic / political circumstances) Irregularity / Residual (random variations, without pattern – external influences)
- Seasonality: variations that repeat over periode (shorter periods) Trend : long term variation Cyclical effect: fluctuations around trend (economic / political circumstances) Irregularity / Residual (random variations, without pattern – external influences)
- Duco Ventilation & Sun Control wanted to lay the groundwork for AI with a first step in IoT. Enabling a more accurate view of its residential ventilation systems’ performance and stakeholders’ experience, Duco saw IoT as the key towards optimizing its products through data-driven processes. Duco needed a solution with multi-location data capture, centralized system monitoring, as well as device and data management – all while providing an enhanced experience for various stakeholder (end user, R&D, partners, field services, …) ACR: 30k/year
- Engie has over 500 renewable energy production sites, including wind turbines and solar panels, collecting billions of messages every day – and counting. They needed a secure, scalable IoT solution to maximize real-time control, minimize lost time due technical issues and intelligent energy production. Pain: High maintenance costs, manage energy streams Solution: Build entire IoT platform to co capture and process data coming out of the SCADA using IoT Edge. ROI: Capture more data in less time with better traceability, and scalable solution. Moreover they can balancing its energy production portfolio based on market data, when it’s most optimal to produce energy. Now making the next steip and bringing in machine learning algorythems and Digital Twin ACR: 250K/Year
- Time Series ID : iothub-connection-device-id
- Job to be done: describe the scenario why the customer needs this Business impact: What are the benefits for the organisation How will the model and solution be used in the entire process of the organisation Which actions and consequences depend on the outcomes of the model Data fuel: Define which data is already available Will the data grow and new data be fed into the system? Do we have labeled data (for supervised learning) or is the data unlabeled Definition of success: What is needed to call the project a success. Describe adoption blockers that need to be tackled, dependencies in the organisation Predict: What do we want to predict Describe the case for the model Indicate the type of prediction (classification, regression (values), clustering, sentiment analysis, etc) Evaluate: Please reflect on the impact in case the model has wrong predictions (False Negatives & False Positives) Should the model focus on overall accuracy (get as much as possible guesses right), or do we have to decrease the amount of False Negatives/Positives for example ? Trust: How long does the model needs to be evaluated and used before it’s considered approved and trusted? Which dependencies do we have on the rest of the processes in order to gain trust Execution: Define where the model should be executed (in the cloud, on the edge, in a device, wherever) Feedback loop: How can the model be monitored and improved, once it’s operational? Who will be monitoring the model and how will feedback be collected? Future scenarios: Related solutions, applications or scenarios that can be made possible