One that would quickly highlight any recent changes or key findings." SAS report summary helps to describe the report in a few sentences to replicate a speech-template. The functionality provides a dynamic description of the report: Conditional text, Dynamic values and Natural Language Generation ... a key requirement if you are going to claim this as AI-enhanced business intelligence. Further examples and details of how to utilize this feature can be found within this article written by Xavier Bizoux. Report summaries are also particularly useful if your audience includes individuals with visual impairments. The report summary can easily be read by screen readers. But wait, there's more ...

Gartner has recognised SAS as a Leader in its 2019 Magic Quadrant for Data Science and Machine Learning Platforms. The report evaluated SAS for its completeness of vision and ability to execute and, for the sixth consecutive year, gave them the rank of Leader in this Magic Quadrant. "Machine learning is a critical tool in the modern data scientist toolbox," says SAS artificial intelligence and machine learning strategist Lorry Hardt. "It allows organisations to quickly identify opportunities for their business, but also avoid risks that may go unnoticed by humans." SAS' evaluation is based on two solutions key to the success of its data scientist users - SAS Visual Data Mining and Machine Learning and SAS Enterprise Miner.

According to SAS, SAS Visual Data Mining and Machine Learning offers users a single platform to solve complex analytical problems. Combining data preparation, visualization, advanced analytics and model deployment, it unifies the entire machine learning process, from data access/transformation and preparation to scoring, in one environment. Running on the SAS Viya engine, SAS Visual Data Mining and Machine Learning includes the latest statistical, machine learning, deep learning and text analysis algorithms that accelerate structured and unstructured data explorations, while also supporting popular open source languages.

It supports the end-to-end data mining and machine learning process with a comprehensive, visual interface that handles all tasks in the analytical life cycle. Plus, since it runs on SAS Viya, the latest addition to the SAS Platform, you get predictive modeling and machine learning capabilities at breakthrough speeds. We hope you enjoy the test-drive! SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. Other brand and product names are trademarks of their respective companies.

Gradient boosting is one of the most widely used machine learning models in practice, with more and more people like to use it in Kaggle competitions. Are you interested in seeing how to use gradient boosting model for classification in SAS Visual Data Mining and Machine Learning? Here I play with the classification of Fisher's Iris flower dataset using gradient boosting, and this may serve as a start point to those interested in trying the classification models in SAS Visual Data Mining and Machine Learning product. Fisher's Iris data is a well-known dataset in data mining. Per Wikipedia, Fisher developed a linear discriminant model to distinguish the species from each other by the features provided in the dataset.

In a previous post I summarized the tasks and procedures available in SAS Viya Data Mining and Machine Learning. In this post, I'll dive into the unsupervised learning category which currently hosts several tasks: Kmeans, Kmodes, and Kprototypes Clustering, Outlier Detection, and a few variants of Principal Component Analysis. In unsupervised learning there are no known labels (outcomes), only attributes (inputs). Examples include clustering, association, and segmentation. Machine learning finds high density areas (in multidimensional space) that are more or less similar to each other, and identifies structures in the data that separate these areas.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight," said Hugo D'Ulisse, Head of Analytical Platform, SAS UK & Ireland.

SAS says its new visual data mining and machine learning software, available later this month, will "feed this need for smarter analytics". The company was speaking about its new solution at its Analytics Experience conference in Las Vegas attended by more than 10,000, on-site and online, to discuss business issues. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS analytics product manager. "This software helps provide positive outcomes to increase profitability, better understand customer behaviour and decrease the cost of doing business." Wexler said advanced analytics offer insight to businesses, but "machine learning and deep learning algorithms take it deeper, insights that were previously out of reach".

At SAS's Analytics Experience conference in Las Vegas yesterday, the analytics powerhouse unveiled two new additions to its SAS Viya analytics platform aimed at supporting cognitive computing: SAS Visual Investigator and SAS Visual Data Mining and Machine Learning. "Cognitive computing is disruptive, combining technologies such as natural language processing, image processing, text mining and machine learning to augment human intelligence," Oliver Schabenberger, SAS executive vice president and CTO, said in a statement yesterday. "SAS has supported cognitive technologies in analytics for decades. The exciting change is applying deep learning and high-performance computing to achieve greater automation and accuracy in the interaction between computers and people." Visual Investigator is a new, cloud-ready investigation and alert management product designed to help intelligence analysts and investigators uncover insider threats, disease outbreaks, loan risks, drug trafficking, fraud and other emerging issues.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software, available later this month, will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS Analytics Product Manager.