For many businesses artificial intelligence (AI) and machine learning are new concepts and therefore can be a challenge to know where to start. Even though most organisations have existing applications and processes that they wish to infuse with AI. Here we explore Microsoft’s AI and machine learning capabilities on offer for businesses via Microsoft Azure and SQL Server to help them prep, build, deploy and manage machine learning models as part of their data and analytics landscape.
With the likes of predictive analytics, chatbots, and natural language processing now available as a service, it’s easier than ever for companies to get on board with machine learning. 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 both on-premise and in the cloud that caters to each of these purposes.
Machine Learning In The Cloud With Microsoft Azure
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.
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 scientist to use it.
- Azure Databricks is an optimised Apache Spark platform in the cloud for heavy analytics workloads, 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 then Microsoft provide 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.
Reasons To Choose Azure Machine Learning Solutions
Now having understood the main components of the Azure machine learning suite, let’s quickly recap the main benefits:
- Flexible pay-as-you-go service and pricing. No need to set up costly infrastructure.
- Fast & flexible development interface supporting a range of open-source frameworks to build machine learning solutions. Options for sophisticated developers to those with no coding experience.
- Access to automation features makes identifying the best algorithms and configuring hyperparameters much faster, improving productivity & reducing costs through autoscaling. Also means you don’t have to be a data scientist to use it.
- Sophisticated pre-built AI and machine learning models via Azure Cognitive Services
- End-to-end machine learning service enabling users to track their models after deployment, making multiple runs to find the best solution and return predictions in real time.
Machine Learning On-Premise With SQL Server
If you still haven’t warmed to the cloud, then Microsoft also provide on-premise options for machine learning via SQL Server Machine Learning Services. SQL Server Microsoft Machine Learning Service adds statistical analysis, data visualisation, and predictive analytics in R and Python for relational data in SQL Server databases. R and Python libraries from Microsoft include advanced modelling and machine learning algorithms, which can run in parallel and at scale, in SQL Server.
Microsoft Machine Learning Server is a standalone enterprise server for predictive analytics, where models can be built and deployed on pre-processed data.
Want To Know More?
Whatever option you choose, Microsoft as always has an extensive range of tutorials and quick starts that can be accessed to help build, deploy and manage machine learning solutions. If you’d like some guidance though, as a Microsoft partner BOOMDATA’s big data and 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 and SQL platforms.
