Megoldások Mind Adattárház Az SAP az Azure-on Big Data jellegű adatok és analitika Biztonsági mentés és archiválás Digitális marketing Digitális média E-kereskedelem Fejlesztés és tesztelés Fejlesztés és üzemeltetés Mobil Nagy teljesítményű számítástechnika Üzletági alkalmazások Vészhelyreállítás
Termék Mind API Management App Service Application Insights Azure Active Directory Azure Bot Service Azure Container Service (AKS) Azure Cosmos DB Azure DevTest Labs Azure Event Hubs-eseményközpontok Azure HDInsighthoz készült Apache Spark Azure Media Player Azure Search Backup Backup Server Batch Bing Beszédfelismerő API Blobtároló Content Delivery Network Data Factory Data Lake Store ExpressRoute Functions HDInsight HockeyApp Language Understanding Intelligent Service (LUIS) Machine Learning Studio Media Services Notification Hubs Power BI Redis Cache Service Bus Service Fabric Site Recovery SQL Data Warehouse SQL Database Storage StorSimple Stream Analytics Traffic Manager Virtual Machine Scale Sets Virtual Network Virtuális gépek Visual Studio Team Services VPN Gateway
Tags Mind AI Anomáliadetektálás Cortana Intelligence Csatlakoztatott autó Lambda-architektúra Megoldás Microsoft R Server Operations Management Price Elasticity Pricing Optimization
Ágazatok Mind Gyártás Pénzügy Egészségügy
Accurately forecasting spikes in demand for products and services can give a company a competitive advantage. This solution focuses on demand forecasting within the energy sector
Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks. Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings add further computational requirements to make real-time pricing decisions, compounding the difficulty of this already overwhelming task.
In an energy grid, energy consumers are engaged with various types of energy supplying, trading, and storage components such as substations, batteries, windfarms and solar panels, micro-turbines, as well as demand response bids, to meet their respective demands and minimize the cost of energy commitment. To do so, the grid operator must determine how much energy each type of the resources should commit over a time frame, given the prices of soliciting different types of resources and the capacities and the physical characteristics of them.
Részletek P ool S torage Virtual Machines Client App W eb App Batch
High performance computing (HPC) applications can scale to thousands of compute cores, extend on-premises big compute, or run as a 100% cloud native solution. This HPC solution is implemented with Azure Batch, which provides job scheduling, auto-scaling of compute resources, and execution management as a platform service (PaaS) that reduces HPC infrastructure code and maintenance.
We want to introduce you to the Cortana Intelligence Vehicle Telemetry Analytics Solution Template. This solution demonstrates how car dealerships, automobile manufacturers and insurance companies can use the capabilities of Cortana Intelligence to gain real-time and predictive insights on vehicle health and driving habits.
The Pricing Analytics solution uses your transactional history data to show you how the demand for your products responds to the prices you offer, to recommend pricing changes, and allow you to simulate how changes in price would affect your demand, at a fine granularity.
This solution demonstrates how to build and deploy a machine learning model with SQL Server 2016 with R Services to recommend actions to maximize the purchase rate of leads targeted by a campaign.