SAS Model Manager - Detailed Review
Data Tools
SAS Model Manager - Product Overview
SAS Model Manager Overview
SAS Model Manager is a comprehensive tool within the Data Tools AI-driven product category, aimed at streamlining and managing the entire analytical modeling life cycle. Here’s a brief overview of its primary function, target audience, and key features:Primary Function
SAS Model Manager is designed to manage, deploy, and monitor analytical models across various environments. It centralizes model management, ensuring that models are properly validated, deployed, and governed throughout their lifecycle.Target Audience
The primary users of SAS Model Manager include data scientists, MLOps engineers, and stakeholders involved in the development, validation, and deployment of analytical models. This tool is particularly useful for organizations that need to integrate and manage models developed in different programming languages such as SAS, Python, and R.Key Features
Unify Model Assets
SAS Model Manager allows you to establish a secure and versioned model registry. This feature enables teams to organize, profile, tag, sort, and categorize modeling assets. You can access models and artifacts through a user interface or programmatically via REST API, SAS code, or Python code, facilitating integration with automated MLOps processes.Validate Models
The tool automatically generates scoring code for Python and R models and ensures that models run correctly in both SAS Model Manager and production environments. It also allows side-by-side comparisons of SAS, Python, and R models to determine the best fit for production.Deploy Models in Minutes
SAS Model Manager enables the deployment of models across various destinations, including cloud, on-premises, Snowpark, and Azure Machine Learning, without the need for recoding. Models can be published into containers saved on registries within the cloud or on premises.Monitor, Detect, Alert & Repeat
The tool provides ongoing monitoring to detect data, concept, and model drift. It offers dynamic performance reporting and allows for the customization of performance reports and KPIs. Alerts can be set up to notify stakeholders or trigger model retraining, minimizing downtime.Streamline MLOps Processes
SAS Model Manager supports Continuous Integration and Continuous Delivery (CI/CD) processes through templates, making it easier to test and promote new models and decision flows. It integrates multiple environments, tools, and applications via a flexible API ecosystem, automating and promoting the right workflows to keep stakeholders informed.Conclusion
In summary, SAS Model Manager is a powerful tool that simplifies the management of analytical models, ensuring they are validated, deployed, and monitored efficiently, making it an essential asset for data-driven organizations. SAS Model Manager - User Interface and Experience
User Interface Overview
The user interface of SAS Model Manager is designed to be intuitive and user-friendly, catering to the needs of data scientists, MLOps engineers, and other stakeholders involved in the analytical modeling life cycle.Main Categories and Interface
The SAS Model Manager interface is organized into five main category views, each represented by an icon in the menu on the left side of the interface. These categories include:Home
Provides an overview of existing projects and models, along with links to additional resources such as documentation, videos, and community forums.Models
Allows users to manage and view all models within the system.Projects
This is where most of the ModelOps activities are performed. Users can create new projects, view existing ones, and manage project-related metadata.Ease of Use
The interface is structured to simplify the model management process. Here are some key aspects that contribute to its ease of use:Data Explorer
This component, available in the full SAS Viya installation and in Open Model Manager, enables users to import, manage, and profile data sets, including CSV files. It provides features like viewing column details, sample data, and data profiles, making data preparation and integration straightforward.Model Validation and Deployment
Users can easily test and validate models using a no-code interface, ensuring models run correctly in both SAS Model Manager and production environments. The deployment process is also streamlined, allowing models to be deployed across various environments without the need for recoding.Monitoring and Alerts
The system offers ongoing monitoring with built-in explainability features, allowing users to detect data, concept, and model drift. It also provides dynamic performance reporting and customizable alerts to stakeholders, which helps in minimizing costly downtime.User Experience
The overall user experience is enhanced by several features:Unified Model Assets
SAS Model Manager allows users to track projects, models, and their supporting artifacts in a secure and versioned model registry. This makes it easy to search, discover, and manage models across the organization using the user interface or programmatically through REST API, SAS code, or Python code.Automated MLOps Processes
The system supports Continuous Integration and Continuous Delivery (CI/CD) pipelines, which automate testing, promotion, and deployment of models. This reduces manual efforts and ensures that the right people are informed at the right time.Integration with Open-Source Tools
SAS Model Manager can manage models produced outside of SAS environments by importing PMML files or managing code from Python and R. This integration is facilitated by tools like PyMAS, which bridges the execution environments between SAS and Python.Conclusion
In summary, the SAS Model Manager interface is well-organized, easy to use, and provides a comprehensive set of features that simplify the entire model life cycle, from creation and validation to deployment and monitoring. This makes it a valuable tool for teams involved in analytical modeling and MLOps. SAS Model Manager - Key Features and Functionality
SAS Model Manager Overview
SAS Model Manager is a comprehensive tool that streamlines the entire analytical modeling life cycle, from creation and validation to deployment, management, governance, and monitoring. Here are the main features and how they work, along with their benefits and the integration of AI:Unify Model Assets
This feature allows you to establish a secure and versioned model registry. Here’s how it works:- You can profile, tag, sort, and categorize your modeling assets, making it easier to search and discover models across your organization.
- Models and artifacts can be accessed through a user interface or programmatically via REST API, SAS code, or Python code, integrating well with automated MLOps processes.
- It manages, versions, scores, and governs models written in SAS, Python, R, and other languages across the enterprise.
- Major events such as model creation, deletion, and deployment are audited, ensuring transparency and accountability.
Validate Models
This feature ensures that your models are reliable and perform as expected:- SAS Model Manager automatically generates scoring code for Python and R models using the sasctl package, simplifying the validation process.
- It ensures that models run correctly within both the SAS Model Manager environment and production environments.
- You can compare SAS, Python, and R models side by side to determine the best fit for production, helping in making informed decisions.
Deploy Models in Minutes
This feature enables quick and efficient model deployment:- Models can be deployed across various destinations, including cloud, on-premise, Snowpark, or Azure Machine Learning, without the need for recoding.
- Models can be published into containers saved on registries within the cloud or on premises, making deployment flexible and efficient.
Monitor, Detect, Alert and Repeat
This feature is crucial for ongoing model performance monitoring:- It illuminates data, concept, and model drift through continuous monitoring, providing dynamic performance reporting from deployment to retirement.
- You can track, validate, and audit reports to select champion models for use in other applications.
- The system synchronizes KPIs to alert stakeholders or cue model retraining, minimizing costly downtime.
- Custom performance reports can be created, and KPIs based on model performance data can be defined to meet specific needs.
Streamline MLOps Processes
This feature automates and integrates various MLOps tasks:- It uses templates to create repeatable Continuous Integration and Continuous Delivery (CI/CD) processes, efficiently testing and promoting new models and decision flows.
- The flexible API ecosystem integrates multiple environments, tools, and applications, automating and promoting models while keeping the right people informed.
- Automated workflows standardize modeling assets for repeatable and customizable automation.
Model Cards and AI Governance
SAS Model Manager also includes features to enhance transparency and governance:- Model cards, akin to “nutrition labels” for AI models, provide detailed information such as accuracy, fairness, and model drift. These cards are auto-generated for registered models and include governance details like last modification, contributors, and responsible parties.
- The AI Governance Advisory services help organizations navigate AI governance, ensuring responsible and ethical AI use. This includes increased productivity, improved trust, and better accountability in data usage.
Integration of AI
AI is integrated throughout SAS Model Manager in several ways:- Automated Model Validation: AI-driven tools automatically generate scoring code and validate models across different environments.
- Model Monitoring: AI-powered monitoring detects data, concept, and model drift, ensuring models remain accurate over time.
- CI/CD Pipelines: AI-driven automation in CI/CD processes streamlines the testing and deployment of models.
- Model Cards: AI-generated model cards provide transparent and detailed insights into model performance and governance.
SAS Model Manager - Performance and Accuracy
Evaluating the Performance and Accuracy of SAS Model Manager
Evaluating the performance and accuracy of SAS Model Manager involves examining its key features, benefits, and any identified limitations.
Performance Monitoring and Accuracy
SAS Model Manager is highly effective in monitoring and ensuring the accuracy of analytical models throughout their lifecycle. Here are some key aspects:
Continuous Monitoring
The software allows for continuous monitoring of model performance, enabling users to detect any degradation in model accuracy due to changes in market conditions, customer behavior, or new data availability. This helps in preventing model decay and maintaining the business value of the models.
Performance Benchmarks
SAS Model Manager generates performance benchmarks and reports, including variable distribution plots, characteristic charts, stability charts, lift charts, ROC charts, and Gini charts. These reports help in comparing the performance of champion and challenger models, ensuring that the best models are in production.
Automated Alerts
The system can set automated alerts to detect changes in scoring results over time, indicating potential model decay. This proactive approach helps in maintaining model accuracy and performance.
Validation and Testing
Before models are exported to production, SAS Model Manager ensures thorough validation and testing of the scoring logic. This includes mapping required inputs and outputs, adding SAS code, and viewing log and results tables, which reduces the risk of incorrect decisions.
Collaboration and Governance
Centralized Repository
The software provides a centralized, secure repository for managing analytical models. This repository includes extensive documentation, version control, and auditability, ensuring that all stakeholders have a unified view of the model’s current stage and can take relevant actions.
Compliance and Auditability
SAS Model Manager supports compliance with regulatory requirements through unique validation and reporting capabilities. It includes Basel II risk model validation reports and provides transparency into analytical processes, making it easier to meet internal governance and external regulations.
Limitations and Areas for Improvement
While SAS Model Manager is comprehensive, there are a few areas where improvements or additional features could enhance its functionality:
Integration with Other Tools
While it supports models from various sources, including SAS, R, and Python, there might be a need for more seamless integration with other data science tools and platforms to cater to diverse user bases.
User Interface and Customization
Although the software offers a web-based client and customization options using SAS Visual Analytics, some users might find the interface or customization options limited. Ongoing updates, such as the new *Compare* page and additional performance monitoring charts, are addressing these needs, but continuous user feedback is crucial.
Training and Support
For users new to model management, there could be a learning curve. Providing extensive training resources and support can help users fully leverage the capabilities of SAS Model Manager.
Conclusion
SAS Model Manager is a powerful tool for managing the lifecycle of analytical models, ensuring their accuracy and performance. Its features in performance monitoring, validation, and governance make it a valuable asset for organizations. However, ongoing updates and user feedback are essential to address any emerging needs and limitations.
SAS Model Manager - Pricing and Plans
Pricing Structure
When it comes to the pricing structure of SAS Model Manager, the information available is limited, as the provider does not publicly disclose specific pricing details.Pricing on Request
SAS does not communicate any price information publicly. This is a common practice for software vendors and service providers. To find out the current prices, you would need to contact the provider directly.Free Trial and Demo
However, SAS Model Manager does offer a free trial version that lasts for 14 days, as well as a demo version. These options can help you evaluate the software before committing to a purchase.Features Across Plans
While the pricing tiers are not specified, here are the key features that SAS Model Manager offers:Unify Model Assets
Establish a secure and versioned model registry, profile, tag, sort, and categorize modeling assets. Access models using the user interface or programmatically through REST API, SAS code, or Python code.Validate Models
Automatically generate scoring code for Python and R models, ensure models run in both SAS Model Manager and production environments, and compare SAS, Python, and R models side by side.Deploy Models in Minutes
Deploy models across various destinations, including cloud, on-premises, Snowpark, or Azure Machine Learning, without recoding.Monitor, Detect, Alert, and Repeat
Monitor for data, concept, and model drift, gain dynamic performance reporting, and track, validate, and audit reports to select champion models.Streamline MLOps Processes
Use templates for Continuous Integration and Continuous Delivery (CI/CD) processes, integrate multiple environments and tools, and automate workflows. Since the specific pricing tiers and their corresponding features are not publicly available, it is best to contact SAS directly for detailed pricing information. SAS Model Manager - Integration and Compatibility
SAS Model Manager Overview
SAS Model Manager is designed to integrate seamlessly with a variety of tools and platforms, ensuring smooth collaboration and efficient model management across different environments.Integration with Open Source Tools
SAS Model Manager allows for integration with open source tools, enabling data scientists, MLOps engineers, and stakeholders to work together effectively. It supports models written in SAS, Python, and R, making it a versatile tool for diverse modeling environments.Multi-Environment Deployment
The platform facilitates the deployment of models across various destinations, including in-database, on-premise, and cloud environments such as Azure Machine Learning and Snowpark. This flexibility ensures that models can be deployed without the need for recoding, saving time and resources.API Ecosystem
SAS Model Manager features a flexible API ecosystem that allows for integration with multiple environments, tools, and applications. This includes REST API, SAS code, and Python code, enabling automated MLOps processes and promoting models efficiently across the organization.Compatibility with SAS Products
The tool is compatible with several SAS products, including Base SAS, which is required but separately licensed. Other recommended but optional products include SAS Data Integration Server, SAS/STAT, SAS/IML, and SAS Enterprise Miner. Additionally, SAS Model Manager includes a standard SAS/ACCESS engine (such as Oracle or PostgreSQL) and supports other engines like DB2, Greenplum, Hadoop, SAP HANA, and Teradata.Operating System Support
SAS Model Manager supports various operating systems, including Windows and Linux. It adheres to a policy of supporting one or more baseline major versions and subsequent minor updates for each officially supported operating system. For example, it supports Red Hat Enterprise Linux 8.4 and subsequent minor updates like 8.7.PMML Support
For models developed in other environments, SAS Model Manager supports the import of PMML (Predictive Model Markup Language) models, specifically PMML version 4.2. However, it’s important to note that PMML 4.4 and certain model types like gradient boosting or random forest may not be fully supported via PMML.Continuous Integration and Continuous Delivery (CI/CD)
The platform streamlines MLOps processes through automated CI/CD pipelines. It provides templates for creating repeatable CI/CD processes, ensuring efficient testing and promotion of new models and decision flows. This automation helps in keeping the right people informed and minimizes manual efforts.Conclusion
In summary, SAS Model Manager is highly integrative and compatible with a wide range of tools, platforms, and operating systems, making it a comprehensive solution for managing and deploying AI models across diverse environments. SAS Model Manager - Customer Support and Resources
Customer Support
If you encounter any issues or have questions about SAS Model Manager, you can contact SAS Technical Support through several channels:
Phone
You can report critical problems by calling the US toll-free number at 1-800-727-0025 or the US Headquarters at 1-919-677-8008.
Chat
There is a chat option available in the lower right corner of your browser, connecting you to SAS Technical Support in the US, Canada, and some US territories.
You can send an email to support@sas.com with detailed information about the problem, including product and version, operating system, site number, and any troubleshooting steps you’ve taken. This ensures quick and efficient service.
Additional Resources
SAS offers various resources to help you get the most out of SAS Model Manager:
User Guides and Documentation
The SAS Model Manager User’s Guide provides detailed information on managing models, including creating, importing, exporting, and publishing models. It also covers model validation, deployment, and monitoring.
Feature Lists and Overviews
The SAS website provides a full feature list and overview of SAS Model Manager, detailing its capabilities such as unifying model assets, validating models, deploying models, monitoring performance, and streamlining MLOps processes.
Tutorials and Videos
There are Quick Start tutorials available, such as the one on the SAS Video Portal, which shows how to deploy and monitor models using SAS Model Manager. These tutorials cover fundamental ModelOps activities like registering champion models and running score tests.
Blogs and Guides
SAS offers blogs and guides that provide additional insights into specific features, such as versioning in SAS Model Manager, registering Python models, and deploying models using CAS.
These resources are designed to help you effectively manage and utilize SAS Model Manager, ensuring you can leverage its full potential in your analytical modeling life cycle.
SAS Model Manager - Pros and Cons
Advantages of SAS Model Manager
Streamlined Model Lifecycle Management
SAS Model Manager simplifies and automates the entire lifecycle of analytical models, from creation and validation to deployment, management, and monitoring. This ensures that models are efficiently registered, validated, deployed, and retrained, reducing manual errors and speeding up the process.
Collaboration and Governance
The tool enables seamless collaboration among data scientists, MLOps engineers, and stakeholders by providing a centralized model repository. This repository includes extensive documentation, version control, and auditability, ensuring transparency and compliance with regulatory requirements.
Validation and Testing
SAS Model Manager offers a no-code/low-code environment for validating model scoring logic, ensuring that models run correctly in both test and production environments. This reduces the risk of incorrect decisions by thoroughly testing the scoring logic before deployment.
Efficient Deployment
Models can be packaged and deployed efficiently across various environments, including in-database, on-premise, or in the cloud, without wasting time. This accelerates the deployment process and ensures models are quickly put into production.
Performance Monitoring and Alerts
The software includes built-in monitoring capabilities that detect data, concept, and model drift. It alerts stakeholders about model decay, allowing for timely actions to be taken to maintain model performance and minimize downtime.
Automated MLOps Processes
SAS Model Manager automates Continuous Integration and Continuous Delivery (CI/CD) pipelines, standardizing modeling assets for repeatable and customizable automation. This maximizes efficiency and reduces manual efforts.
Transparency and Explainability
The tool provides “model cards” or “nutrition labels” for AI models, assessing their accuracy, fairness, and transparency. This feature helps in maintaining trust and compliance in AI-driven decision-making processes.
Disadvantages of SAS Model Manager
Limited Customer Base
While SAS Model Manager is a powerful tool, it has a relatively small customer base compared to other data tools. For example, in the network monitoring category, it has a negligible market share and a very small number of customers.
Integration Dependencies
To fully leverage its capabilities, SAS Model Manager may require integration with other SAS products or external platforms like Azure Machine Learning. This can add complexity and dependency on additional software components.
Learning Curve
Although the tool offers a no-code/low-code environment, it still requires a certain level of technical expertise to fully utilize its advanced features. This can be a barrier for organizations without experienced analytics professionals.
Cost
SAS products are generally known to be on the higher end of the cost spectrum, which can be a significant disadvantage for smaller organizations or those with limited budgets. However, specific pricing details for SAS Model Manager are not provided in the available resources.
In summary, SAS Model Manager offers significant advantages in streamlining model lifecycle management, enhancing collaboration, and ensuring model performance and compliance. However, it may have limitations in terms of its customer base, integration requirements, and potential cost.
SAS Model Manager - Comparison with Competitors
Unique Features of SAS Model Manager
- Comprehensive Model Lifecycle Management: SAS Model Manager streamlines the entire analytical modeling lifecycle, from creation and validation to deployment, management, governance, and monitoring. This includes tracking projects, models, and their supporting artifacts, ensuring clear model lineage and versioning.
- Efficient Deployment: The tool allows for quick model deployment across various environments, including in-database, on-premise, or in the cloud, with minimal manual effort.
- Automated CI/CD Pipeline: SAS Model Manager maximizes efficiency through automated Continuous Integration and Continuous Delivery (CI/CD) pipelines, standardizing modeling assets for repeatable automation.
- Model Validation and Monitoring: It ensures models run correctly in both development and production environments, with built-in explainability and alerts for model drift and decay.
- Governance and Transparency: The tool provides “model cards” to assess the accuracy, fairness, and transparency of AI models, ensuring responsible AI deployment.
Alternatives and Competitors
Databricks
- Databricks is a popular alternative that focuses on unified data analytics, combining data engineering, data science, and data analytics in one platform. While it excels in big data processing and machine learning, it may not offer the same level of model governance and lifecycle management as SAS Model Manager.
- Key Difference: Databricks is more geared towards data engineering and large-scale data processing, whereas SAS Model Manager is specialized in model management and deployment.
IBM Watson Analytics
- IBM Watson Analytics offers automated pattern detection, natural language query support, and advanced analytics capabilities. However, it lacks the detailed model lifecycle management and governance features of SAS Model Manager. Watson Analytics is more focused on automated insights and visualization rather than comprehensive model management.
- Key Difference: Watson Analytics is stronger in natural language inquiries and automated pattern detection but weaker in model deployment and governance.
RapidMiner
- RapidMiner is another competitor that provides a comprehensive data science platform for data preparation, machine learning, and model deployment. While it offers a wide range of features, its model management capabilities are not as specialized or extensive as those of SAS Model Manager. RapidMiner is more general-purpose and may require additional configuration for detailed model governance.
- Key Difference: RapidMiner is more versatile but may not match the depth of model management and governance provided by SAS Model Manager.
Alteryx
- Alteryx is known for its user-friendly interface and ability to automate data science tasks. However, it is more focused on data preparation and analytics rather than the detailed model lifecycle management and deployment that SAS Model Manager offers. Alteryx lacks the advanced governance and monitoring features of SAS Model Manager.
- Key Difference: Alteryx excels in data preparation and automation but is not as strong in model management and governance.
Conclusion
SAS Model Manager stands out for its comprehensive approach to model lifecycle management, including creation, validation, deployment, and governance. While alternatives like Databricks, IBM Watson Analytics, RapidMiner, and Alteryx offer strong capabilities in data analytics and machine learning, they do not match the specialized features and depth of model management provided by SAS Model Manager. If your primary need is robust model governance and lifecycle management, SAS Model Manager is a strong choice. However, if you require a more general-purpose data analytics platform, one of the alternatives might be more suitable. SAS Model Manager - Frequently Asked Questions
What is SAS Model Manager and what is its primary purpose?
SAS Model Manager is a software tool designed to streamline the entire lifecycle of analytical models, from creation and validation to deployment, management, governance, and monitoring. Its primary purpose is to manage and govern analytical models as high-value assets, ensuring they are accurate, up-to-date, and compliant with regulatory requirements.
Who is SAS Model Manager designed for?
SAS Model Manager is designed for various stakeholders, including model validation and compliance analysts, IT professionals responsible for model deployment, analytic professionals concerned with model performance, and business unit heads who need to apply analytical insights for decision-making.
How does SAS Model Manager unify model assets?
SAS Model Manager establishes a secure and versioned model registry, allowing teams to profile, tag, sort, and categorize modeling assets. It enables search and discovery of models across the organization and provides access to models and artifacts through a user interface or programmatically via REST API, SAS code, or Python code.
What are the key steps in validating models using SAS Model Manager?
The validation process involves automatically generating scoring code for models, ensuring models run correctly in both SAS Model Manager and production environments. It also includes comparing SAS, Python, and R models side by side to determine the best fit for production and validating model scoring logic through a no-code interface.
How does SAS Model Manager facilitate model deployment?
SAS Model Manager allows for the deployment of SAS, Python, and R models across various destinations, including cloud, on-premise, Snowpark, or Azure Machine Learning, without requiring recoding. Models can be published into containers saved on registries within the cloud or on premises.
What monitoring and alert capabilities does SAS Model Manager offer?
The software provides ongoing monitoring to detect data, concept, and model drift. It offers dynamic performance reporting from deployment to retirement and allows for the customization of performance reports and the creation of KPIs based on model performance data. Alerts can be set to notify stakeholders of model decay, minimizing costly downtime.
How does SAS Model Manager streamline MLOps processes?
SAS Model Manager streamlines MLOps processes by using templates to create repeatable Continuous Integration and Continuous Delivery (CI/CD) processes. It integrates multiple environments, tools, and applications through a flexible API ecosystem, automating and promoting new models and decision flows efficiently.
What kind of reporting and auditing capabilities does SAS Model Manager provide?
SAS Model Manager offers extensive reporting and auditing capabilities, including model validation reports, Basel II risk model validation reports, and performance benchmarking reports. It provides a centralized model repository, lifecycle templates, and version control, ensuring visibility and auditability of analytical processes to comply with internal governance and external regulations.
Can SAS Model Manager handle models from different programming languages?
Yes, SAS Model Manager supports the management, versioning, scoring, and governance of models written in SAS, Python, R, and other languages. It allows for the registration and deployment of models from these different environments.
How does SAS Model Manager ensure accountability and version control?
SAS Model Manager provides accountability metrics and version control status reports, tracking changes to models, including who made the changes, when, and why. It also includes event logging and the ability to attach documents for further transparency and auditability.
What kind of user interface and workflow management does SAS Model Manager offer?
SAS Model Manager offers a Web-based client, the SAS Workflow Console, which provides an automated and collaborative model management process. Users can define and track custom workflows for model lifecycle management using the SAS Workflow Studio, ensuring a unified view of the model’s current stage and access to meaningful information for taking relevant actions.
SAS Model Manager - Conclusion and Recommendation
Final Assessment of SAS Model Manager
SAS Model Manager is a comprehensive tool that streamlines the entire lifecycle of analytical models, from creation and validation to deployment, management, governance, and monitoring. Here’s a detailed look at its benefits and who would most benefit from using it.Key Features and Benefits
Unify Model Assets
SAS Model Manager allows users to establish a secure and versioned model registry, enabling easy search, discovery, and management of models across the organization. It supports models written in SAS, Python, R, and other languages, and provides access through a user interface or programmatically via REST API, SAS code, or Python code.
Validate Models
The tool ensures models run correctly in both SAS Model Manager and production environments. It automatically generates scoring code for Python and R models and allows side-by-side comparison of different models to determine the best fit for production.
Deploy Models in Minutes
SAS Model Manager facilitates quick deployment of models across various environments, including cloud, on-premises, and in-database systems like Teradata, without the need for recoding. Models can be published into containers saved on registries within the cloud or on premises.
Monitor, Detect, Alert & Repeat
The tool offers ongoing monitoring to detect data, concept, and model drift. It provides dynamic performance reporting and allows customization of performance reports and KPIs. Automated alerts can be set to notify stakeholders of model decay, minimizing downtime.
Streamline MLOps Processes
SAS Model Manager automates and integrates Continuous Integration and Continuous Delivery (CI/CD) processes, reducing manual efforts. It enables collaboration and keeps stakeholders informed through a flexible workflow ecosystem.
Who Would Benefit Most
Model Validation and Compliance Analysts
These professionals can use SAS Model Manager to review and validate models, ensuring they comply with stated specifications and regulatory requirements.
IT Professionals
IT teams responsible for preparing data for model deployment and validating score code will find the tool invaluable for ensuring models run correctly in operational environments.
Analytic Professionals
Those concerned with the performance degradation of analytical models already in production can use SAS Model Manager to monitor and retrain models efficiently.
Business Unit Heads
Leaders who want to apply analytical insights to make better decisions continuously can benefit from the streamlined model management and deployment processes offered by SAS Model Manager.
Overall Recommendation
SAS Model Manager is highly recommended for organizations that need to manage and deploy analytical models efficiently. It provides a unified framework for model lifecycle management, ensuring models are accurate, up-to-date, and compliant with regulatory requirements. The tool’s ability to streamline MLOps processes, automate model deployment, and monitor model performance makes it an essential asset for data scientists, MLOps engineers, IT professionals, and business leaders.
By using SAS Model Manager, organizations can expedite the deployment of “best” models into production, ensure auditability and compliance, and maintain the integrity and performance of their analytical models over time. This leads to more timely and accurate decision-making, which is crucial for business success.