Businesses across Australia are increasingly investing in artificial intelligence. According to Gartner, AI and machine learning will become critical components for future success, predicting that by 2020, augmented analytics will be a dominant driver of new purchases of business analytics platforms. As the need for data analytics increases, a desire for predictive automation also grows alongside. For many Australian firms, machine learning holds the key – and Microsoft Azure could well be the primary data platform for advanced analytics, moving forward.
Why Use Microsoft Azure For Advanced Analytics?
For many businesses, machine learning is still a very new concept. Some executives may previously have assumed training a system to handle menial tasks to be an uphill struggle. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviours, outcomes, and trends. By using machine learning, computers learn without being explicitly programmed. Machine learning solutions are built iteratively and have distinct phases:
- Preparing data
- Experimenting and training models
- Deploying trained models
- Managing deployed models
Microsoft provides a variety of products on-premise or in the cloud to help prep, build, deploy and manage your machine learning models. We’ll focus on Microsoft Azure in the cloud for our purposes here.
Microsoft Azure is a flexible, scalable and hybrid public cloud platform providing enterprises with components required to build, manage and deploy applications including managing their business intelligence, advanced analytics and Big Data solutions.
Microsoft Azure presents users with a machine learning system which is flexible with a wealth of apps and suites.
What is likely to be very appealing to Australian businesses is Azure’s cost-effectiveness — embracing something as groundbreaking as industrial AI may conjure up thoughts of escalating software bills. However, Microsoft merely requests that you pay for the service as and when you need to.
Extensive AI and Machine Learning Support
Azure has a machine learning suite to suit most use cases. For those wanting to do their own coding, there is Azure Machine Learning Services and Azure Databricks.
- Azure Machine Learning Services (AML Service) is an end-to-end managed cloud-based machine learning solution that allows users to create, teach, launch and manage their own machine learning models on any scale using a range of open-source frameworks like Python and CLI. Even though coding is required, the product’s automation feature means that you don’t have to be a developer and data scientists to use it.
- Azure Databricks is an optimised Apache Spark platform in the cloud for heavy analytics workloads. It was designed with the founders of Apache Spark, allowing for a natural integration with Azure services. Databricks makes the setup of Spark as easy as a few clicks allowing organisations to streamline development and provides an interactive workspace for collaboration between data scientists, data engineers, and business analysts. Developers can enable their business with familiar tools and a distributed processing platform to unlock their data’s secrets. While Azure Databricks is a great platform to deploy AI Solutions (batch and streaming), it can also be used as the compute for training machine learning models before deploying with the AML Service (web service). Use Databricks when you want to collaborate on building machine learning solutions on Apache Spark.
Additionally, if you want to use pre-built AI and machine learning models, Azure Cognitive Services is a set of APIs that allows you to easily add intelligent features to your applications, like to allow your apps to see, hear, speak, understand and interpret user needs with just a few lines of code. These add:
- Emotion and sentiment detection
- Vision and speech recognition
- Language understanding (LUIS)
- Knowledge and search
For those of us new to AI and machine learning and not great at coding, Microsoft provides the Azure Machine Learning Studio.
Azure Machine Learning Studio
Not to be confused with Machine Learning Service, Azure Machine Learning Studio is a simpler platform, a visual workspace which lets users create machine learning solutions using a drop-and-drag browser-based system, with no coding required. Allowing users to click their way from initial idea to deployment using Azure’s prebuilt and preconfigured algorithms and data modules. Users of the Machine Learning Studio can dive right into designing and building AI resources without any prior knowledge. This service is seen as the entry-level option for many business users new to AI and is the perfect way to build confidence and to hone skills.
Always Ready for Deployment
Another highlight of Azure’s suite and Studio is just how simple it is to deploy the AI module you create. Using extensive tutorials and presets, users can test their services before launching directly to the web. This simple deployment will allow the vast majority of devices to take advantage of code within seconds.
Microsoft Azure is quickly becoming the bastion of entry-level AI training. As demand for efficiency increases, so will workload pressure on human control. Therefore, the need for businesses, across all industries, to embrace AI and machine learning is paramount. Azure presents a superb library of resources, tutorials and ideas, as well as opportunities for users to grow and develop their custom services moving forward.
Inside Info’s big data and advanced analytics services are designed to amplify analytics beyond the descriptive to deliver even further insight for businesses. Our advanced analytics services can include designing and delivering predictive analytics, machine learning, real-time & IoT analytics and conversational analytics solutions for clients based on the Microsoft Azure platform.
Sonia Johnson heads Inside Info’s Marketing team, as an experienced B2B marketer, having launched and built the Qlik brand in the Australian market. Sonia has 20 years’ experience working within the IT and telco industries, having worked for IBM and Vodafone, the last ten years have been focused within the business intelligence and corporate performance management sectors.