Machine Learning, we know for sure, is not hype. It is real. It is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time. In other words, it enables computers to learn over time without being explicitly programmed. What is then Azure Machine Learning or in other words Azure ML?
As we know, Azure is not new to the AI + ML domain. For many years, Azure has been offering numerous disparate services in this domain such as bot & cognitive services and many additional services of partnering vendors. Azure ML is one of the Azure services that Microsoft has been proudly boasting about. It is an enterprise-grade machine learning service that stands out by offering unique capabilities to structure and accelerate the end-to-end machine learning lifecycle across an organization.
With digitization and automation, the application of ML models throughout organizations has been widening as never before. Together with the increased regulatory scrutiny, organizations are forced to look for efficient ways of developing and validating models in an end-to-end manner.
Many organizations have already taken steps towards implementing some sort of overarching machine learning practices. However, the journey to an organization-wide adoption is a road full of challenges. And it seems like Azure Machine Learning can certainly play a role in addressing some key pain points with its holistic approach.
Azure Machine Learning In A Nutshell
Azure ML is a cloud-based environment you can use to train, deploy, automate, manage, and track ML models across an organization. It supports machine learning of any kind from classical ML to deep learning, supervised, and unsupervised learning. It provides a studio for developers to write Python or R code with the SDK. Microsoft has not neglected citizen developers such that the studio supports no-code/low-code options as well.
Access your data centrally
It is not a secret that one of the key challenges organizations face is around the data. The data resides scattered throughout the whole organization. When the start is already so much filled with difficulties, thinking ahead is a nightmare for organizations.
The first and foremost challenge of any organization lies around its data. The data in an organization lies generally in silos across the departments without a solid control over what is where, who is using what, and when. Of course, Azure Machine Learning cannot solve this problem in one go. This is a deeply rooted problem in an enterprise and falls under the domain of data architecture. However, with Azure Machine Learning solutions, firms can access their data anywhere and anytime. It enables the collection of data from a wide range of data sources. It supports direct data connection with sources like Hive Query, Azure SQL, on-premises data sources, and many more.
Integrated development environment
One of the key value propositions of the Azure ML is its integrated development environment called ML Studio. Users without a data science background can also build data models through drag-and-drop gestures and simple data flow diagrams. It contributes to minimizing coding on one hand, but also saves a lot of time through ML Studio’s library of sample experiments on the other hand.
Azure Machine Learning provides a huge collection of the best-of-breed algorithms developed by Microsoft Research to derive regression, clustering, and predictive scenarios. This is an extremely useful feature that gives Azure a great competitive advantage. With many well-known algorithms configurable in a drag-and-drop manner, it is certainly coming out as an amazing feature.
The third utmost challenge of organizations is around development and deployment via monitoring, validation, and governance of machine learning models. MLOps, or DevOps for machine learning is responding to this need by enabling data science and IT teams to collaborate in a DevOps fashion.
Additionally, Azure Machine Learning supports end-to-end ML lifecycle management using open MLflow standards. Users can submit training jobs using MLflow experiments and Projects. And switch the configuration to run models on Azure ML, when ready to scale to the cloud. Since you register the models and track them in a central registry using MLOps, it makes it enormously easier to deploy to Azure Container Instance or Azure Kubernetes Service.
Azure Machine Learning is the Cloud.
Last but not least, it is important to note that Azure ML is a fully cloud-based solution. Use of it does not require a lengthy implementation process. It comes with a cloud data analytics solution to ensure that you keep your operating expenses to a minimum. With this, you manage better resource allocations for Azure Machine Learning Compute with workspace and resource level quota limits. Through small monthly payments, you eliminate your CAPEX and upfront fees. More importantly, it delivers comprehensive machine learning services that have add-on benefits of the cloud for increased security, business agility, and innovativeness.