SAS Model Manager and the sasctl packages aim to create a seamless ModelOps and MLOps process for Python and R models. Python and R models are not second-class citizens within SAS Model Manager. SAS, Python, and R models can be easily managed using our no-code/low-code interface. This is an interface that can be extended to support a variety of use cases.
Tag: sas model manager
Attend this session during the SAS Explore event on Sept 27-29 or view the recording at your convenience. We will showcase the use of SAS Intelligent Decisioning, SAS Model Manager, and SAS Visual Analytics on the SAS Viya platform for a solution that helps mitigate inequitable credit decisions.
Just getting started with this series? Make sure to explore the earlier posts Part 1, Part 2 and Part 3. Up until now, you have seen how ModelOps can solve your biggest machine learning challenges and that SAS and Microsoft, together, can help you deploy, govern and monitor your models
Just getting started with this series? Make sure to explore Part 1 and Part 2. There are different ways you can use these two tools to accelerate model building, deployment and monitoring. Figure 1 summarizes best practices for conducting ModelOps using SAS Model Manager and Azure Machine Learning. Best practice
Just getting started with this series? Make sure to read part 1: How ModelOps addresses your biggest Machine Learning challenges. SAS and Microsoft make it easier for companies to address the challenges of machine learning model deployment, monitoring and governance. Specifically, SAS and Microsoft have built integrations between SAS® Model
This post, written by Radhikha Myeni and Jagruti Kanjia, will demonstrate how easy it is to build and deploy a machine learning pipeline by using SAS and Python. The Model Studio platform provides a quick and collaborative way to build complex pipelines by dragging and dropping nodes from a web-based
SAS and Microsoft are working tirelessly to improve offerings and connectivity between SAS Viya and Microsoft Azure environments across industries.
Most model assessment metrics, such as Lift, AUC, KS, ASE, require the presence of the target/label to be in the data. This is always the case at the time of model training. But how can I ensure that the developed model can be applied to new data for prediction?
How do you deploy your model so that business processes can make use of it? This post explores how SAS Viya applications can directly add models to a model repository, and specifically focuses on how to deploy them with SAS Model Manager to Hadoop.
As you heard in Sunday’s Opening Session, SAS reinvests more than a fifth of total revenue in R&D. According to Mark Torr, Global Technology Practice Director, that investment goes into enhancements, updates and new products that SAS® users – you – request. Torr and several SAS employees performed live demos