Azure solution architectures

Campaign Optimisation with Azure HDInsight Spark Clusters

This solution demonstrates how to build and deploy a machine learning model with Microsoft R Server on Azure HDInsight Spark clusters to recommend actions to maximise the purchase rate of leads targeted by a campaign. This solution enables efficient handling of big data on Spark with Microsoft R Server.

Customer 360

A deep understanding between customer interests and purchasing patterns is a critical component of any retail business intelligence operation. This solution implements a process of aggregating customer data into a “360 degree” profile and uses advanced machine learning models backed by the reliability and processing power of Azure to provide predictive insights on simulated customers.

Big compute with Azure Batch

Big compute and high performance computing (HPC) workloads are normally compute intensive and can be run in parallel, taking advantage of the scale and flexibility of the cloud. The workloads are often run asynchronously using batch processing, with compute resources required to run the work and job scheduling required to specify the work. Examples of Big Compute and HPC workloads include financial risk Monte Carlo simulations, image rendering, media transcoding, file processing and engineering or scientific simulations.

Build high availability into your BCDR strategy

Virtual machines (VMs) are physically separated across zones and a virtual network is created using load balancers at each site. These locations are close enough for high availability replication, so your applications stay running, despite any issues at the physical locations.

Cross cloud scaling with Azure and Azure Stack

Modern software is increasingly connected and distributed. The consistency of Azure Stack with Azure infrastructure and platform services enable you to scale resources cross cloud to meet increased load as needed and decrease resources as demand drops. Optimise cost and maximise resource efficiency while remaining compliant with cross cloud architecture.

Energy Supply Optimisation

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 minimise 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.

Information Chatbot

This Informational Bot can answer questions defined in a knowledge set or FAQ using Cognitive Services QnA Maker and answer more open-ended questions using Azure Cognitive Search.

Instant Broadcasting on Serverless Architecture

Simplify one-to-many real-time communication and updates using serverless code