Microsoft Azure grants businesses the power of cloud technology and replaces the traditional on-premises infrastructure that companies were reliant upon many years ago. Fast forward to today, we see AI quickly gaining momentum in the digital world as a powerful solution to many of today’s digital challenges. Entering the fray, Microsoft has invested in AI to bring innovative features to their solutions; a popular example being Bing’s new AI-powered capabilities thanks to ChatGPT. Azure’s AI features have also been extensively augmented thanks to AI advancements.

By implementing such AI features, we will see more efficient ways of working and collaborating. We explore a few of the options that Azure AI can help businesses make smarter decisions faster.

Azure Bot Service

With Azure Bot Service, users are able to manage, create, and launch chatbots with ease thanks to the extensive features that are available. Thankfully, Azure chatbots are available with out-of-the-box functionalities to get users started. Chatbots are particularly useful as answering client queries, presenting information from CRM and other data sources, translating, offering personalised recommendations, and moderating content.

AI-powered chatbots reduce the load on support departments by removing the reliance on telephone helplines and can improve the customer experience with quick problem solving and information gathering. When Azure Chatbots are connected with Azure Cognitive Services, it will benefit from natural language and conversational language understanding, image analysis, and more.

Azure Machine Learning Service

Customers looking to build their own machine learning solutions can use the Azure Machine Learning Service which enables them to create, teach, launch, and manage their own ML models. It can also be used as a base upon which to build to get faster results. As an end-to-end ML solution, users can manage and track models following deployment, achieve the best solution with several runs, and analyse predictions in real-time.

Azure ML Service is automated which means it identifies the best algorithms and configures hyperparameters with speed, thereby raising productivity and lowering costs through autoscaling. This also means that its accessible to smaller companies that want to start with machine learning without having a dedicated data science team. It is fully compatible with container services like Azure Container Instances, Azure Kubernetes Service, and Docker, making it easy to transport your ML solution.

Azure Machine Learning Studio

Azure Machine Learning Studio offers a simpler visual platform in which is create machine learning solutions. With an emphasis on drag-and-drop features, there are no coding requirements to get started. IT is also browser-based, so users can build upon an idea using Azure’s preconfigured algorithms and data modules.

With collaboration features built into ML Studio, teams can work together to build, test, and deploy their new ML solution. Despite offering a user-friendly interface, ML Studio offers the same powerful tools as ML Service. As long as the preconfigured algorithms are fit for your model, ML Studio is the perfect tool to get started.