Introduction to Time Series Analytics with Microsoft Azure

Editor's Notes

• #4: https://wall.sli.do/event/mr4f4kug?section=fc9aec73-be4f-4146-b1be-f18b8dcae466
• #8: 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)
• #9: 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)
• #13: 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
• #14: 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
• #22: Time Series ID : iothub-connection-device-id
• #25: 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