As organizations mature analytically, it’s important for the platform to support multiple roles in a common interface with a unified data infrastructure. This strengthens collaboration and makes it easier for people to do their jobs.
Is it possible to reduce your current BI / Analytics solution TCO and, on the other hand, to increase the availability and flexibility of the solution? CILIX’s BigData services enable you to do so, with short term results, and a gradual project in order to guarantee an effective knowledge transfer. By hiring our specialized professionals, the usage of Apache Hadoop ecosystem tools and partnerships with the SAS are the core ingredients to make your optimization project successful.
Is it possible to increase my installment sales without jeopardizing my cash flow due to high insolvency and chargeback? Through the usage of consumption data (water, power and electronic invoice), social networks and banking information (list of compliant debtors) as well as state of the art machine learning and BigData tools to collect and process data, we provide an assertive credit analysis, without increasing risks.
SAS Visual Data Mining and Machine Learning, which runs on SAS® Viya®, combines data wrangling, exploration, feature engineering and modern statistical, data mining and machine learning techniques in a a single, scalable in-memory processing environment. The solution provides a very visual and highly collaborative workspace that supports a variety of users with different skill sets.
Don't know SAS code? No problem. SAS Visual Data Mining and Machine Learning lets you embed open source code within an analysis, and call open source algorithms seamlessly within a Model Studio flow. This facilitates collaboration across your organization, because users can program in the language of their choice. The new node in Model Studio is agnostic to Python or R software versions; any version can be used as the code is passed.
Superior performance from massive parallel processing and the feature-rich building blocks for machine-learning pipelines let you explore and compare multiple approaches rapidly. You can 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. The solution also lets you 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 analytical life cycle delivers answers and insights you can trust.
Data scientists and other analytical professionals can get highly accurate results from a single, collaborative environment that supports the entire machine-learning pipeline. The solution enables a variety of users to 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 you can do all this faster than ever before.
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. Standard interpretability reports are also available in all modeling nodes, including LIME, ICE, PD plots, etc.