SAS Model Manager Reviews & Product Details

I use SAS Model Manager for insights and getting relevant data. I like that it helps accelerate model deployment and streamline the MLOPE processes. What I appreciate most is the AI maturity model and how it integrates the people, technology, and process model into a cohesive cycle. Additionally, the integration with Viya and other tools is quite beneficial. The initial setup was also kind of easy, which is a plus. Review collected by and hosted on G2.com.

Making it more aware for the public and real world use cases Review collected by and hosted on G2.com.

The interface is straightforward and clean. Review collected by and hosted on G2.com.

It seems a bit not easy to customize and the incorporation with python seems complicated Review collected by and hosted on G2.com.

My favorite part about Model Manager was the ability to automate processes that would otherwise take a lot more time & energy in just R or Python. For example, the train and validation split of the data is automatically taken care of when running the model pipeline. Same goes for comparing the model outputs (Accuracy, area under ROC, etc.). Review collected by and hosted on G2.com.

Parameter tuning wasn't as intuitive (at least from the perspective of someone who is more familiar with coding this whole process). I thought it was difficult to understand many of the parameter tuning options, as well as hard to search for the specific tweaks I was looking to do. Review collected by and hosted on G2.com.

I love how simple Model Manager made working with classical ML models. Traditionally these models are made at the start of every task when working with data as an elementary step, but manual coding takes up a decent amount of time. With Model Manager I was pleased to see how simple the process was and how it even provided comparisons and tons of accuracy metrics. Review collected by and hosted on G2.com.

I would have preferred more documentation. With every new tool there is a learning curve but figuring out the small details relied on my own efforts of exploring the site for a while. There is a lot of documentation and instruction overall with SAS but it is hard to navigate and find the specific information I need. Review collected by and hosted on G2.com.

Model manager allows me to view all my models in a singular place. Allows me to share and collaborate with others in my team Review collected by and hosted on G2.com.

. But better functionality in viewing who owns the model. Who has done updates to the model in an audit capability would be good Review collected by and hosted on G2.com.

Sas model manager helps deploy the models ,Alert genration and validation support for created models ,It works as an automated process for continuies integration and deliver for the piplines Review collected by and hosted on G2.com.

As we can integrate other model created with help of R and Python but for debugging those we need the expertise with those languages Review collected by and hosted on G2.com.

It is easy to develop and deploy analytical model using SAS Model Manager, many techniques, including machine learning and statistical modeling are available to data scientists.

SAS Model Manager allows us to monitor the performance of deployed models over time. This includes tracking key performance indicators, such as accuracy, precision, and recall, to ensure that models continue to provide accurate and reliable predictions.

SAS Model Manager includes version control capabilities, allowing users to track and manage different versions of a model. This is crucial for maintaining a clear audit trail and reverting to previous versions if needed.

The tool enforces governance policies to ensure that models adhere to regulatory and organizational guidelines.

SAS Model Manager can be deployed in the cloud (AWS).

SAS Model Manager integrates with other SAS Viya components and SAS solutions, providing a seamless workflow for end-to-end analytics processes.

With automation and workflow management capabilities, SAS Model Manager helps streamline the model development and deployment process, reducing the time and effort required.

SAS Model Manager allows for the retirement of models that are no longer in use or have been replaced by newer versions. This ensures that only relevant and up-to-date models are actively deployed. Review collected by and hosted on G2.com.

Even though SAS Model Manager provides a lot of value to organization, it is not a cheap product. Review collected by and hosted on G2.com.

The main advantage of the SAS model manager is user friendly capabilities, someone who's not a tech expert can navigate through it effortlessly. Its seamless integration capability which plays well with other tools and systems. let's say you have a dashboard in SAS Visual Analytics and you want to use a model you've developed. With SAS Model Manager, it's as simple as a few clicks to deploy and implement that model into your dashboard. Review collected by and hosted on G2.com.

Since SAS model manager coming with lot of features sometimes it is difficult for the new users to take the advantage of those features for some of their specific use case Review collected by and hosted on G2.com.

SAS is very powerful analytics and data management tool. The full analytics lifecycle including Data management model development and decision making model deployment is fully covered by VSDS Visual Data Science decisioning Review collected by and hosted on G2.com.

Visual data science decisioning is a great and vast experience so I can't share the dislike of that because it has already provided robust experience. Review collected by and hosted on G2.com.

It allows us to restructure our workflow and evaluate rules. Despite stability and documentation concerns the advantages it brings outweigh any drawbacks. Review collected by and hosted on G2.com.

The drag-and-drop functionality is convenient it limits customization options. Make fine-tuning models more challenging. Review collected by and hosted on G2.com.