5 Reasons why Azure ML for Machine Learning solutions From virtual assistants to chatbots and automation to smart homes, Artificial Intelligent (AI) and Machine Learning have become prominent in our daily life. According to International Data Corporation (IDC) forecasts, spending on AI and ML will grow to $57.6B by 2021 from $12B in 2017. According to the report by Statista, by 2020, AI-based advanced analytics solutions market will reach $70 million. Small and medium businesses (SMB) see the potential growth and future in Machine Learning. However, cost, special coding skills, and hardware are the biggest factors for them. Fortunately, Azure offers Azure ML to provide the robust environment for the development of Machine Learning solutions. SMB can jump into the Machine Learning easily and start gaining its benefits without a plunge into it greatly. Let’s have a look at the most significant reasons to choose Azure ML for your Machine Learning solutions. Use Machine Learning as a Service Microsoft Azure offers Azure Machine Learning as a pay-as-you-go service. Using Azure ML, the businesses do not require setting up complex or purchasing any big hardware or software. They just need to purchase the services and can start developing their Machine Learning applications immediately. Read how a US based Smart Meter manufacturer & Service provider goes cutting-edge with Smart Analytics system for Utilities. The advanced analytics engine built around the water consumption data enabled the utilities and its customers in taking better decisions for water conservation. The system helped connect 500,000 smart meters and analyze 1 Bn+ data records/day. Easy & Flexible building interface Business can execute their Machine Learning development through the Microsoft Azure Machine Learning Studio. It offers drag and drop components that minimize the code development and straightforward configuration of properties. Moreover, it helps businesses to build, test and generate advanced analytics based on the data. Wide range of supported algorithms Azure ML offers readily available well-known algorithms that can be configured simply by drag and drop. It does not require the knowledge of data science or expertise in algorithms; you just need to know when to use them. Specific algorithms like logistic regression and decision tree can also help in devising real-time predictions or forecasts. Moreover, there is no limit in importing training data and you can fine-tune your data easily. This feature of Azure ML significantly saves the cost and help to generate revenue. Easy implementation of web services All you need is to drag and drop your data sets, algorithms, and link them together to implement web services needed for ML development. Once you have practiced with the environment, you just need to test, which would be ready for the further use, then you need to click one button to create and publish the web service. Now, you can use the web service from any device by passing valid credentials. A Manufacturing company running Spark Machine Learning solutions on their local servers wanted to move their data & ML solutions to Microsoft Azure cloud for better scalability and ease of maintenance. Read the success story on how our team leveraged Azure HDInsight Spark, an Azure hosted offering of open-source Spark, that provides complete common data science tasks such as data ingestion, feature engineering, modeling, and model evaluation. Great documentation Microsoft Azure offers full stacks of documentation like quick starts, tutorials, references, and lots of examples that help businesses to easily build, deploy, manage and access the ML solutions effectively. In addition to the above, Azure ML offers the utmost flexibility and extensibility with the feature to include R and Python code. It is beneficial when the built-in Machine learning algorithms and models are not enough. Conclusion Managing and utilizing big data is always a cumbersome task for enterprises. However, building advanced analytics solutions with Azure ML is more approachable and simple. The finest part is they do not require in-house expertise and yet, avail the benefits of analytics solutions with the help of Machine Learning developers through connecting the data sets, algorithms, and modules.