Enable everyone to work in the same integrated environment – from data management to model development and deployment.
Easily solve complex analytical problems with automated insights.
SAS Visual Data Mining and Machine Learning automatically generates insights that enable you to identify the most common variables across all models, the most important variables selected across models, and assessment results for all models. Natural language generation capabilities are used to create a project summary written in simple language, enabling you to easily interpret reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members.
Empower users with language options.
Don't know SAS code? No problem. SAS Visual Data Mining and Machine Learning lets you embed open source code within an analysis, call open source algorithms within a pipeline, and access those models from a common repository – seamlessly within Model Studio. This facilitates collaboration across your organization, because users can do all of this in their language of choice. You can also take advantage of SAS Deep Learning with Python (DLPy), our open source package on GitHub, to use Python within Jupyter notebooks to access high-level APIs for deep learning functionalities, including computer vision, natural language processing, forecasting and speech processing. DLPy supports the Open Neural Network Exchange (ONNX) for easily moving models between frameworks.
Explore multiple approaches quickly to find the optimal solution.
Superior performance from massive parallel processing and the feature-rich building blocks for machine learning pipelines let you explore and compare multiple approaches rapidly. Quickly and easily find the optimal parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option you want. Complex local search optimization routines work hard in the background to efficiently and effectively tune your models. You can also combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types. And reproducibility in every stage of the analytics life cycle delivers answers and insights you can trust.
Boost the productivity of your analytical teams.
Data scientists, business analysts and other analytics professionals get highly accurate results from a single, collaborative environment that supports the entire machine learning pipeline. A variety of users can access and prepare data. Perform exploratory analysis. Build and compare machine learning models. Create score code for implementing predictive models. Execute one-click model deployment. And do it all faster than ever before with our automated modeling API.
Reduce latency between data and decisions.
To enhance collaborative understanding, the solution provides all users with business-friendly annotations within each node describing what methods are being run, as well as information about the methods, results and interpretation.
Interpret models using simple language.
Standard interpretability reports are available in all modeling nodes, including LIME, ICE, Kernel SHAP, PD heatmaps, etc., with explanations in simple language from embedded natural language generation. Export modeling insights as a PDF report that can be shared outside the data science team.
Try the latest SAS® Viya® capabilities
Get a free, 14-day trial of SAS® Visual Data Science Decisioning, which includes all the capabilities of SAS® Visual Data Mining and Machine Learning as well as for the entire analytics life cycle.
A comprehensive visual – and programming – interface supports the end-to-end data mining and machine learning process. Analytics team members of all skill levels are empowered to handle all analytics life cycle tasks in a simple, powerful and automated way.
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- White Paper The Machine Learning LandscapeThis paper, for novice and intermediate data scientists, talks about the four widely recognized machine learning styles and their common uses, data and modeling methodologies, and popular algorithms for solving machine learning problems.
- White Paper How to Do Deep Learning With SAS® Get an introduction to deep learning techniques and applications, and learn how SAS supports the creation of deep neural network models.
- White Paper Statistics and Machine Learning at ScaleDiscover how machine learning can be applied to a variety of applications, and learn about the techniques required to help computers learn from massive amounts of data.
- Customer Story Cancer treatment enters a new era with artificial intelligenceAmsterdam UMC uses analytics and AI solutions from SAS to increase speed and accuracy of tumor evaluations.
- Analyst Report Gartner positions SAS as a Leader in the Magic Quadrant for Data Science and Machine Learning Platforms, Q1 2021Gartner positions SAS as a Leader in the Magic Quadrant for Data Science and Machine Learning Platforms for the eighth consecutive year.
- Customer Story Forecasting population and need for supplies in refugee camps For the 2020 SAS® Hackathon, a team from Notilyze demonstrated how SAS® Viya® and external data sources can provide insights into the population in refugee camps.
- Analyst Report SAS is a Leader in The Forrester Wave™: Multimodal Predictive Analytics & Machine Learning, Q3 2020.Forrester report states that products evaluated are SAS® Visual Data Mining and Machine Learning and SAS® Model Manager.
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