Kartik Patel
What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals.
With the right tools, today’s average business user can become a Citizen Data Scientist, using data integrated from various sources to learn, test theories and make decisions. AutoML comes into play as business users leverage systems and solutions that are designed with Machine Learning capabilities to predict outcomes and analyze data.
Take for example, the task of performing predictive analytics. Business users can leverage machine learning and assisted predictive modeling to achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. Business users can take advantage of AutoML tools to explore patterns in data and receive suggestions to help them gain insight – all without dependence on IT or data scientists.
This Predictive Modeling capability is combined with auto-recommendations and auto-suggestions to simplify use so that business users can work with sophisticated predictive algorithms in an intuitive, easy-to-use environment and apply advanced analytics to use case using forecasting, regression, clustering and other methods to assess customer churn, target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.
AutoML is, quite simply, the automated process of features and algorithm selection that supports planning, and allows users to fine tune, perform iterative modeling, and allows for the application and evolution of machine learning models.
In its report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’, published on October 31, 2018, Gartner provided the following strategic planning assumption: ‘By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI as well as data science and machine learning platforms, and of embedded analytics.’
Machine learning takes the heavy lifting away from business users and allows them to leverage their core business knowledge and skills to engage in predictive analytics, while allowing the flexibility and sophistication of machine learning to offer the guided assistance of a ‘smart’ solution. The system interprets the dataset, selects important columns, analyzes categories, types and other parameters and uses intelligent machine learning to automatically apply the best algorithm and analytical technique and provide data insights.
Machine Learning Algorithms allows the system to understand data and applies correlation, classification, regression, or forecasting, or whichever technique is relevant, based upon the data the user wishes to analyze. Results are displayed using visualization types that provide the best fit for the data, and the interpretation is presented in simple natural language. This seamless, intuitive process enables business users to quickly and easily select and analyze data without guesswork or advanced skills.
Not so long ago, this type of Advanced Analytics would have demanded the services of a full-time, trained data scientist. Today, augmented data science and machine learning automates and democratizes key aspects of data science, predictive analytics and machine learning, and reduces the need for analysts and data science skills to generate, manage and collaborate using advanced analytic models. As AutoML and assisted predictive modeling evolves, business users, and the organizations they support, will benefit with increased productivity, improved knowledge management and more refined planning, predictions and outcomes.
