UNIX and Linux SAS^® administrators, have you ever been greeted by one of these statements as you walk into the office before you have gotten your first cup of coffee? Power outage! SAS servers are down. I cannot access my reports. Have you frantically tried to restart the SAS servers to avoid loss of productivity and missed one of the steps in the process, causing further delays while other work continues to pile up? If you have had this experience, you understand the benefit to be gained from a utility that automates the management of these multi-tiered deployments. Until recently, there was no method for automatically starting and stopping multi-tiered services in an orchestrated fashion. Instead, you had to use time-consuming manual procedures to manage SAS services. These procedures were also prone to human error, which could result in corrupted services and additional time lost, debugging and resolving issues injected by this process. To address this challenge, SAS Technical Support created the SAS Local Services Management (SAS_lsm) utility, which provides automated, orderly management of your SAS^® multi-tiered deployments. The intent of this paper is to demonstrate the deployment and usage of the SAS_lsm utility. Now, go grab a coffee, and let's see how SAS_lsm can make life less chaotic.

Users want more power. SAS^® delivers. Data grids are a new data type available to users of SAS^® Business Rules Manager and SAS^® Decision Manager. These data grids can be deployed to both batch and web service scoring for data mining models and business decisions. Users will learn how to construct data with grid data types, create business rules using high-level expressions, and deploy decisions to both batch and web services for scoring.

As a data scientist, you need analytical tools and algorithms, whether commercial or open source, and you have some favorites. But how do you decide when to use what? And how can you integrate their use to your maximum advantage? This presentation provides several best practices for deploying both SAS^® and open-source analytical tools to increase productivity and efficiency in your enterprise ecosystem. See an example of a marketing analysis using SAS and R algorithms in SAS^® Enterprise Miner to develop a predictive model, and then operationalize that model for performance monitoring and in-database scoring. Also learn about using Python and SAS integration for developing predictive models from a Jupyter Notebook environment. Seeing these cases will help you decide how to improve your analytics with similar integration of SAS and open source.

SAS^® Decision Manager includes a hidden gem: a web service for high-speed online scoring of business events. The fourth maintenance release of SAS^® 9.4 represents the third release of the SAS^® Micro Analytics Service for scoring SAS^® DS2 code decisions in a standard JSON web service. Users will learn how to create decisions, deploy modules to the web service, test the service, and record business events.

Machine learning is not just for data scientists. Business analysts can use machine learning to discover rules from historical decision data or from historical performance data. Decision tree learning and logistic regression scorecard learning are available for standard data tables, and Associations Analysis is available for transactional event tables. These rules can be edited and optimized for changing business conditions and policies, and then deployed into automated decision-making systems. Users will see demonstrations using real data and will learn how to apply machine learning to business problems.