How to Deploy SAS and Open-Source Models to Azure Machine Learning with SAS Model Manager
In 2020, SAS and Microsoft announced their partnership with the common goal to inspire greater trust and confidence in every data-driven decision, by driving innovation and proven AI in the cloud.
Artificial Intelligence (AI) is changing the way people and organizations improve decision-making and move about their lives — from text translation to chatbots and predictive analytics. However, many organizations are struggling to realize its potential as model deployment processes remain disconnected, creating unforeseen headaches and manual work. Additionally, other requirements like performance monitoring, retraining, and integration into core business processes must be streamlined for optimal teamwork and resource usage.
With a combined product roadmap, SAS and Microsoft are working tirelessly to improve offerings and connectivity between SAS Viya and Microsoft Azure environments across industries, and further shape the future of AI and Analytics in the Cloud.
In this blog, we will explore how to publish models written in SAS and open-source language (such as python) in Azure Machine Learning, using SAS Model Manager, the SAS’ tool to operationalize, manage, deploy and govern all types of models everywhere, providing an industry approved framework for ModelOps implementation.
From there, we will see how data scientists can enrich their applications with SAS or open-source models within their Azure environment.
This integration will enable users to:
1) Extend SAS models stored in SAS Model Manager into the Azure Machine Learning registry, offering more opportunities for collaboration across the enterprise.
2) Deploy SAS and open-source models from SAS Model Manager to Azure Machine Learning on the same Azure Kubernetes cluster you have already set up in Azure Machine Learning. Before deploying the model, you can validate the model and ensure it meets your criteria.
3) Seamlessly connect your SAS Viya and Microsoft environments without the hassle of verifying multiple licenses with single sign-on authentication via Azure Active Directory (Azure AD).
Step 1: To get started, use Azure AD for simplified SAS Viya access
Step 2: SAS Model Manager governs, deploys, and monitors all types of SAS and open-source models (i.e., Python, R). On Models and Home Pageviews, you can see the models you and your team are working on in addition to versioning, where are they deployed
and “How to” /” What’s new” videos with the latest updates.
Step 3: Compare different models to identify the most accurate “champion model.” Deploy the model throughout the Microsoft ecosystem from cloud to edge with customizable runtimes, centralized monitoring, and management capabilities.
Step 4: Using the provided artifacts, Azure Machine Learning creates executable containers supporting SAS and open-source models. You can use the endpoints created through model deployment for the scoring of the data.
Step 5: Schedule SAS Model Manager to detect model drift and automatically retrain models in case of poor performance or bias detection.
If you want to know more about SAS Model Manager and SAS integration capabilities with Microsoft and open-source, check out the resources below:
- “What’s New with SAS Model Manager” article seriesto find out the latest and greatest releases.
- SAS and open source integration interactive ebook, to learn about how users can use open-source with SAS to achieve trusted decisions from data
- See how the SAS Hackathon teams used SAS Viya on Azure to solve 100 different use cases on Data for Good, industries and startups.
Let us know what you think!
We would love to hear what you think about this new experience and how we can improve it. If you have any feedback for the team, please share your thoughts and ideas in the comments section below.
About the author on SAS Model Manager:
As a Marketing Manager for data science and open-source, Marinela Profi uses her cross-domain expertise in statistics, business and marketing, to position SAS as a leader in the Data Science and Machine Learning Platform market.
She is a keynote speaker and presenter at different global conferences, where she shares trends and priorities of the data science industry. She is a published author, contributor to several eBooks, and blog writer on major industry and data science blogs.
Previously, she worked as an Advanced Analytics Engineer, developing solutions for banking, retail, and manufacturing industries. Her main contributions have been around the combination of forecasting and machine learning techniques.
And last but not least, she is a proud woman in tech. She helps other women feel empowered to join the tech industry and work in areas related to data science and machine learning, by contributing to initiatives like Women in Analytics and WomenTechNetwork.
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