Azure Machine Learning — reasoning your whys and what-ifs
You might have taken your first step towards implementing machine learning in your organization by planning to do so. But you might also think to back off because you find potential challenges in adopting machine learning into your business practices. You see the shortcomings like difficulty in collecting data as it lies scattered in the entire organization and difficulty in accessing data from wherever you desire. Even if a business could overcome these hurdles, deploying new machine learning models in production systems often requires months of engineering investments. Scaling, managing and monitoring machine learning systems require the capabilities of a highly sophisticated data engineering organization as machine learning requires thorough training and expertise of data scientists.
This is where machine learning platforms come into the picture.
Solution to all your machine learning chagrins
Microsoft created a machine learning solution to allow organizations to build their own ML flows that cover collecting data, cleansing, processing, feature engineering, and training machine learning models.
Microsoft Azure Machine Learning empowers companies to overcome the challenges they face in deploying and using machine learning. It delivers comprehensive machine learning services that have add-on benefits of the cloud. Azure Machine Learning services enable its partners to build data-driven applications to provide their end-clients with predictions and forecasting to enhance several business processes.
How Azure Machine Learning is unique in its offerings
Apart from the fact that it comes with easy deployment, it has many features that make the system exceptional. Azure ML allows users to collect 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.
It enables data experts to create machine learning models using R while using Python for data processing via Python script modules. Azure Machine Learning provides many well-known algorithms in a drag and drop manner, which can then be configured directly into the system.
Azure Machine Learning has a large collection of the best-of-breed algorithms developed by Microsoft Research to derive regression, clustering and predictive scenarios which gives it a competitive advantage in the market.
Algorithm classes used in Azure Machine Learning
Classification
Classification is a method that uses data to determine the category, type, or class of an item or row of data. It can be used to classify email filters as spam or junk, categorize customers by their tendency to respond to a sales campaign, or identify sentiment as positive or negative.
Regression
Regression algorithms are typically used to predict values such as sales figures, product demand, and marketing returns as it forecasts the future by estimating the relationship between variables.
Clustering
Clustering uses iterative techniques to group cases in a dataset into clusters that contain similar characteristics. It is used for a variety of machine learning tasks, such as detecting abnormal data, clustering of text documents, and analysis of a dataset prior to using other classification or regression methods.
Why organizations should adopt Azure Machine Learning services
Access data anytime, anywhere, and on any device
Gone are the days when files were kept on a single server or on a single computer. With Azure Machine Learning solutions, firms can have access to their data anywhere, anytime. This feature offers an interactive view of business data powered by data visualization through mobile access in no time, and irrespective of where you are. Azure ML is a fully cloud-based solution, so companies can start using it without a lengthy implementation process. Being able to access reports anywhere and on any device is one of the major benefits of cloud services. This allows businesses to easily engage with remote workers, provide flexible working arrangements, and allow staff to access work materials without them being present in the office.
Greater integration
Adopting a cloud-based solution creates multiple opportunities for simplified integration with various providers. Microsoft’s Power BI is an amazing tool that enables businesses to achieve more with visualization capabilities. It is now powered with Azure ML and R which allows companies to apply machine learning on their data and directly visualize the results using Power BI.
Learn more: How Power BI along with Azure ML and R help you visualize and interact with your data
Insights
Today businesses need to make this data easily accessible across the organization. With the advancement of information technology competition has grown and businesses need to adopt fast emerging technologies to stand out.
Increased business agility
Azure Machine Learning offers organizations an opportunity to make smarter, more precise and timely decisions. They are no longer based on guesses or intuitions, but on all the data they collect. It enables organizations to dig deeper into their data and uncover key insights that will improve their business processes and decision-making.
Cost effective
Azure Machine Learning offers cloud data analytics solution which ensures that your operating expenses are kept to a minimum. Large capital investments and upfront fees can be eliminated and replaced with small monthly payments.
Get started with Azure Machine Learning
Often building data models is not the only challenge. There are several challenges in the initial stages of machine learning itself that deals with data preparation, structuring, sorting and so on. A dedicated data team is required to address such challenges.
Our expert data team and visualizers can help your organization with analytics capabilities powered by Azure Machine Learning. Our approach involves developing a solution that matches your business needs, the kind and amount of data that you gather and based on your existing IT infrastructure setup. Talk to our machine learning experts to derive data-driven decisions for business growth.
Originally published at www.softwebsolutions.com on May 10, 2018.
