ServusNet currently has an on-premises solution which uses daily weather forecasts to generate the final wind power forecasts at a farm level. ServusNet would now like to deploy an end to end operationalized cloud-based solution so that they can scale out their product offering, catering to multiple wind farm portfolios and customers globally.
With Microsot’s advanced ML algorithm embedded in Azure ML, ServusNet is now able to provide a world-class, reliable and scalable cloud-based solution that predicts the power generated by wind farms
Source: ServusNet Forecasts Wind Power Using Cortana Analytics Suite – Machine Learning – Site Home – TechNet Blogs
The demand in Internet of Things (IoT) Analytics Market is highly driven by the increasing penetration of connected devices and analytics tools. Furthermore, the shifting interests in cloud deployment, predictive analytics for business, end-to-end automation, and consumer-friendly IoT analytics platform are some other factors driving this market and creating value in the market.
via Internet of Things (IoT) Analytics Market Worth $16.35 Billion by 2020 – MarketWatch.
Machine learning is the process of teaching a computer to impose structure and meaning on data. Currently Machine learning is “on fire” and with it we’re seeing an amazing new possibilities formerly reserved for science fiction.
- Improving elevator efficiencies
- Predicting Grocery delivery of different foods and house goods
- Predicting the Outcome of the 2014 U.S. Congressional Elections
- Personalized experience in the retail industry for all interactions and touch points
- Retail Fraud
- Price Optimization
- Inventory Forecasting
- Location Analytics
- Heat map for breeding cows
Breeding cows can be tricky. The window for successful insemination is narrow – 12 – 18 hours every 21 days – and spotting it can require farmers to monitor tens or even hundreds of cows.
In Japan, dairy farmers employed a high-tech solution to noticing when a cow was getting frisky.
Azure Stream Analytics brings a unique perspective in the overall complex event processing and real-time processing space with the following customer benefits:
- Low cost: Stream Analytics is architected for multi-tenancy meaning you only pay for what you use and not for idle resources.
- Faster developer productivity: Stream Analytics allow developers to use a SQL-like syntax that can speed up development time from thousands of lines of code down to a few lines. The system will abstract the complexities of the parallelization, distributed computing, and error handling away from the developers.
- Elasticity of the cloud: Stream Analytics is built as a managed service in Azure. This means customers can spin up or down any number of resources on demand. Customers will not have to setup costly hardware or install and maintain software.