Portfolio

Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and staying upright while ice skating. Below, you can find a variety of Sophia's past writings, notebooks, and sessions.

All Things Open

• ATO 2022: A Data Scientist’s and ML Engineer’s Guide to ModelOps Session - n a recent survey, 42% of data scientists reported that their results were not used by decision-makers. When this work goes unused, it is demotivating. Ultimately, data scientists want to apply the models they have developed to solve problems and make better decisions. Data scientists want to make an impact. But despite all this work and investment, there is still a gap between finding insights and using insights. Enter ModelOps. In this session, Sophia Rowland, Product Manager of ModelOps at SAS, will introduce ModelOps, provide steps for getting started, and walk through best practices. This session will address how to bridge the gap between finding insights and using insights to improve outcomes through improved decision-making through analytics.

National Consortium for Data Science

• Supporting AI Risk Management in the Analytics Lifecycle - The National Institute of Standards and Technology (NIST) released an AI Risk Management Framework for trustworthy and responsible use of AI and analytics. NIST offers a portfolio of measurements, standards, and legal metrology to provide recommendations that ensure traceability, enable quality assurance, and harmonize documentary standards and regulatory practices. Their framework is very detailed with recommendations across four functions: govern, map, measure, and manage. In this session, we’ll discuss incorporating these recommendations into the analytics lifecycle. Attendees to this session will gain a greater understanding of trustworthy AI best practices as well as user roles and expectations for building responsible analytics.

MLOps Community Podcast

• Extending AI: From Industry to Innovation with Sophia Rowland & David Weik - Sophia Rowland & David Weik joined the MLOps Community Podcast to share learnings and best practices from implementing MLOps and LLMOPs at organizations across industries, around the globe, and using various types of models and deployments, from IoT CV problems to composite flows that feature LLMs.

Webinars

• Best Practices for Adopting Containers within your MLOps Process Webinar - With the release of SAS Container Runtime (SCR), organizations can execute models and decisions outside of SAS using standard technologies. Containerized deployments are lightweight to save on cloud costs, portable to enable easy movement across environments, and scalable to meet traffic needs. In this talk, we will discuss how IT and MLOps Engineering teams can create repeatable Continuous Integration/Continuous Delivery (CI/CD) processes to efficiently test and promote new models and decision flows using containers.
• How Do I Use SAS® Model Manager? Ask the Expert Webinar - Join us as we walk through the steps for registering models and modeling metadata from various interfaces, running scoring tests, defining performance monitoring reports, publishing models and much more.
• Why Do I Need SAS Intelligent Decisioning and SAS Model Manager to Achieve Analytics Success? - In this webinar, we’ll talk about how SAS Model Manager supports ModelOps and MLOps processes and how SAS Intelligent Decisioning helps build transparent and automated decision flows.

SAS Explore

• SAS Explore Opening Session 2022 - CTO Bryan Harris reviews how the right analytics and AI platform can help businesses navigate and plan for disruption in the market by driving productivity and business outcomes. Demos include SAS Studio, Model Studio, SAS Model Manager, and more.
• Unified ModelOps and Containerized Deployment for All Models Session - SAS® Model Manager establishes standardized ModelOps processes to traverse the last mile of analytics and get value from analytical investments.

SAS Innovate

• Enforcing Trustworthy AI Standards in Analytical Projects - Learn how SAS tools simplified The National Institute of Standards and Technology's (NIST) AI Risk Management Framework adoption. Gain insight into how models align with fairness and responsibility principles, and trustworthy AI practices and user roles.

Open Data Science

• ModelOps, MLOps, and Finding Value in Analytics - Data scientists want to make an impact. But despite all their work and analytical investments, there is still a gap between finding insights and using insights. Enter MLOps and ModelOps. (Co-Author)

The SAS Data Science Blog

• An Easy Button for Data Science - Do you ever wish there was an easy button on data science? Do you ever think to yourself, "I want a solution that could examine the importance of missing variables, create new features, and build models with the best possible performance?" I am still looking for that easy button. However, the Data Science Pilot Action Set comes pretty close.
• Everything But the Kitchen Sink Feature Generation - Using the featureMachine action, we were able to create many new features and using the selectFeatures action, we narrowed our features down into a usable number.
• Automated Machine Learning Pipelines - The dsAutoMl action is all that and a bag of chips! It will explore your data, generate features, select features, create models, and autotune the hyper-parameters of those models.
• SAS and R Integration for Machine Learning - R first appeared in 1993 and has gained a steady and fiercely loyal fan base. But as data sets become both longer and wider, storage and processing speeds become an issue. Having spent weeks whipping an extremely wide and messy data set into shape using only R, I am so grateful for SAS Viya and not having to go through that again. SAS Viya is a cloud-enabled, in-memory analytics engine which allows for rapid analytics insights. SAS Viya utilizes the SAS Cloud Analytics Services (CAS) to perform various actions and tasks. Best of all, CAS is accessible from various interfaces including R.
• SASPy for Modeling - SAS Viya is great for coding in open source languages. Don't believe me? Then check out my previous blog on machine learning with R (and check back later for data science in Python on Viya). An area I haven't touched on yet is integrating open source and SAS 9.4. But, the wait is over! I would like to introduce SASPy, the package for building a bridge between SAS 9.4 and Python. Some of you may be saying "Well, wait SASPy is nothing new to us 9.4 natives!" Which is true. SASPy has been on GitHub since 2015.
• Easier Feature Generation and New Data Science Pilot Actions - In the middle of my blog series, SAS released Viya 3.5. Included in Viya 3.5 was the release of Visual Data Mining and Machine Learning (VDMML) 8.5, which includes several new Data Science Pilot features and actions. Therefore, I will go over the two new actions, the new SAS Studio task, and the new node for the visual Model Studio interface. As a result of these additions, the Data Science Pilot action set is more powerful and feature generation has just gotten even more easier and accessible!
• An Easy Button for Data Science in Python - This blog will go over the Data Science Pilot Action Set from Python.
• Maximize model performance without maximizing effort - In SAS Visual Data Mining and Machine Learning, adding hyperparameter autotuning only requires an extra line of code or a button click. While SAS searches the hyperparameter space in the background, I am free to pursue other work. Hyperparameter autotuning is now one of my favorite machine learning capabilities (right next to feature machines). Through hyperparameter autotuning, I am maximizing my model's performance without maximizing my effort.
• Automated Machine Learning Vs. The Data Scientist - Ever since automated machine learning has entered the scene, people are asking, "Will automated machine learning replace data scientists?" I personally don't think we need to be worried about losing our jobs any time soon. Automated machine learning is great at efficiently trying a lot of different options and can save a data scientist hours of work. The caveat is that automated machine learning cannot replace all tasks. Automated machine learning does not understand context as well as a human being. This means that it may not know to create specific features that are the norm for certain tasks. Also, automated machine learning may not know when it has created things that are irrelevant or unhelpful.
• Portfolio Optimization using SAS and Python - Analytics can be categorized into four levels: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics explore what happened, diagnostic analytics examine why something happened, and predictive analytics imagine what will happen. Today, we will focus on the final level, prescriptive analytics, which tries to find the best possible outcome. Specifically, we will focus on using optimization to select a stock portfolio that maximizes returns while taking risk tolerance into account.
• Operationalizing Consumer Complaint Monitoring - The Text Investigation Framework utilizes several technologies built on SAS Viya, providing a well-integrated and open solution for operationalizing text analytics. As a follow-up to our last post, we will discuss a key application of the framework relevant to customers of all industries – monitoring customer complaints.
• Utilizing Text Models with Ease - As an example, I am frequently thinking about how to productionalize and operationalize text analytics. Many organizations are still focusing on operationalizing their classification and prediction models, often leaving text models as science experiments or ad hoc jobs. But with the release of SAS Viya 2020.1.4, text categories and concept models can now be deployed into production with just a few clicks and used to score data in-batch and via API! You can also now use these models in decision flows. In today’s article, let me show you how easy it is to put these models into production and utilize these models for decisioning.
• SAS and Microsoft Certifications for Data Scientists - I’ve had the opportunity to earn several SAS and Microsoft certifications, so in today’s article, I want to share my thoughts around each one to help you decide which is right for you!
• Embedding real-time decisioning through APIs - This post discusses both decision flows and APIs as they relate to embedding decision flows into webpages and applications using APIs.
• Embedding real-time decisioning through the Microsoft Power Platform - In this post, we create a seamless, efficient, and enjoyable loan approval or pre-approval process using SAS and the Microsoft Power Platform.
• Generating word embeddings - Text data requires processing to create structured data from documents in a corpus. There are numerous techniques available for text processing and text analytics, but today we will focus on generating word embeddings.
• Get started with ModelOps now - You can start learning about ModelOps and SAS Model Manager now. This article breaks up the variety of educational resources provided by SAS based on your learning preference.
• MLOps for Pirates and Snakes: The Sasctl Packages for R and Python - 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.
• Embedding responsible AI best practices within the model lifecycle - NIST sits under the U.S. Department of Commerce and their mission is to promote innovation and industrial competitiveness. NIST offers a portfolio of measurements, standards, and legal metrology to provide recommendations that ensure traceability, enable quality assurance, and harmonize documentary standards and regulatory practices. While these standards are not mandatory or compulsory, they are designed to increase the trustworthiness of AI systems. This framework is very detailed with recommendations across four functions: govern, map, measure, and manage. This blog will focus on a few of these recommendations and where they fit within a model’s lifecycle.
• The Ultimate Must-Have for MLOps: SAS Viya - A recent article came out with an updated list of necessary components for MLOps and LLMOps. And while this list may seem long, reading through the capabilities and components, I realized that SAS Viya already covers most of the required functionality. Organizations can have a hodgepodge of tools that they have to string together themselves or an analytics platform that already works with your favorite container registry.

SAS Communities

• Building Control Charts for SAS Visual Analytics on Viya - Out-of-the-box, SAS Visual Analytics on Viya does not include a control chart object. In addition, the reference lines in the time series object are static – they will not dynamically change as the data changes. But that is not a problem for us! To create a dynamic control chart, meaning a chart that changes the average, UCL, and LCL lines, based on a rolling date period, we will build a custom graph, make calculated items, and filter the chart by date. In-depth step-by-step instructions on creating controls charts in SAS Visual Analytics on Viya are below!
• Building Performance Monitoring Processes in SAS Workflow Manager - SAS Workflow Manager is a tool on SAS Viya that allows users to build out defined processes for their analytical lifecycle. These workflows consist of a defined series of tasks. SAS Workflow Manager can systemically route tasks requiring manual intervention while automatically triggering system tasks and alerts. This allows teams to embed manual checks-and-balances while automating where they can. The processes built into SAS Workflow Manager can be fully customized to fit your business procedures. In this post, we will start by understanding the components of SAS Workflow Manager before moving on to create a performance monitoring workflow.
• Tips and Tricks for Scoring Data with Python Models in SAS 9.4 - Starting with SAS 9.4M6, PROC FCMP enabled SAS users to execute Python within their SAS code. This enables users to switch between coding in SAS and Python as well as make use of Python models in execution environments written in SAS. In today’s post, I want to walk you through how to utilize PROC FCMP to score data using a Python model from SAS 9.4 in four easy steps.
• Using SAS Viya Published Models in SAS 9.4 - This method accesses models published into SAS Viya’s Micro Analytic Service (MAS) using PROC HTTP. In today's post, we will score data in SAS 9.4 using a model sitting inside of MAS on SAS Viya in four steps.
• Scoring Text Using SAS Intelligent Decisioning - Tapping into unstructured text data can glean illuminating insights. But discovering the insights buried in text to build better decisions can often be a manual process. With the right tools, this process can be completely automated and even API-enabled. I propose a solution based on SAS Intelligent Decisioning that can take text from an API call, process the text using a model built in SAS Visual Text Analytics, and return a decision based on the results of the text model.
• Using SAS Viya ASTORE Score Code in SAS 9.4 - This solution is aimed at organizations that want to take models built within SAS Viya and use SAS 9.4 to execute scoring. SAS Viya and SAS 9.4 can act as complements to form one analytical platform. These two can interoperate in a variety of ways, but today we will focus on taking ASTORE Score Code from Viya to run in SAS 9.4.
• Using SAS Viya Data Step Score Code in SAS 9.4 - SAS Viya and SAS 9.4 interoperate in a variety of ways. This post focuses on scoring data in SAS 9.4 using Data Step score code developed in SAS Viya.
• Sending Emails Using SAS Intelligent Decisioning and Python - Given the flexibility SAS Intelligent Decisioning offers, its available capabilities can be easily augmented through the available DS2 or Python code files nodes. An easy capability to add is the ability to send emails. These emails can alert staff of customers to follow-up with or of suspicious activity for investigation. These emails can also inform customers of new and interesting offers. Today we will go over how to add emailing to your decisioning flow using Python code files.
• Git as a SAS Model Manager Publishing Destination - Configuring Git as a publishing destination in SAS Model Manager takes just a few steps. In this article, we prepare a Git repository, configure a Git publishing destination on SAS Viya, and publish a model to Git.
• Creating Custom Fraud Monitoring KPIs in SAS Model Manager - With the release of SAS Viya 2021.1.1, users now have the ability to define their own Key Performance Indicators (KPIs) for model monitoring. The MMKPI Macro and Action Set allow ModelOps teams to define KPIs that are important to their specific use cases. There are numerous options for domain-specific KPIs, but today I want to focus on utilizing this new feature for fraud operations.
• What’s New with SAS Model Manager? SAS Container Runtime Publishing to Google Cloud Platform - With the January release on SAS Viya 2021.2.3, ModelOps engineers can deploy, execute, and validate SAS Container Runtime (SCR) containers in the Google Cloud Platform (GCP). (Co-Author)
• What's new in SAS Model Manager? Share models, projects and assets on Microsoft Teams - With the release of SAS Model Manager on 2021.1.6, we have implemented a new easy way to share what you are working on with your teammates. A new sharing icon has appeared in the upper right corner of the interface, which allows users to easily share models, projects, and specific views via a link or directly to Microsoft Teams! (Co-Author)
• Build MLOps Workflows – An improved user experience with Model Manager - In today's blog, I want to share some of the newest additions to SAS Workflow Manager over the past months. (Co-Author)
• Using SASCTL package to deploy and manage MLFlow models - a step by step introduction - In this blog, we will explore what is SASCTL and introduce the new integration with MLflow. (Co-Author)
• Move model assets from Git for deployment and monitoring – a step by step introduction using SASCTL - In this blog, we will briefly discuss sasctl as well as introduce its integration with git. (Co-Author)
• Easy Deployment and Monitoring for Multinomial Classification Models - SAS Model Manager fully supports binary classification and prediction models already. But with the release of SAS Model Manager 2021.2.6, we’ve baked-in broader support for multinomial classification models. (Co-Author)
• SAS for Customer Analytics & Real-Time Interaction Management: Q2 2022 Analyst Viewpoints - We are excited to announce that in the second quarter of 2022, SAS was recognized as a Leader in two influential reports released by Forrester Research relevant to the martech and business analytic industries. (Contributor)
• ModelOps, MLOps, and Finding Value in Analytics - ately, data scientists want to apply what the models they have developed to solve problems and make better decisions. Data scientists want to make an impact. But despite all this work and investment, there is still a gap between finding insights and using insights. Enter MLOps and ModelOps. (Co-Author)
• Connect with ModelOps Experts and Technologists at SAS Explore - There will be plenty of opportunities to learn more about ModelOps and containerized deployment for models and decisions at SAS Explore.
• Have Questions About SAS Container Runtime? Ask the Experts! - Organizations can greatly benefit by adopting containerized deployments of models and decisions, but where do they start? SAS is providing a free webinar on October 12th to discuss how IT and MLOps Engineering teams can use SCR to create repeatable Continuous Integration/Continuous Delivery (CI/CD) processes to efficiently test and promote new models and decision flows.
• Have You Mastered ModelOps? Show Off Your Skills with New Certification - To keep up with industry trends, SAS recently released a certification centered on ModelOps! In today’s article, I want to share my thoughts around the new certification and point you to resources to help you prepare!
• Easily Promote and Use Containerized Model Deployments - IT and ModelOps Engineering teams can use SAS Container Runtime to create repeatable Continuous Integration and Continuous Delivery (CI/CD) processes to efficiently test and promote new models and decision flows. To make this process even easier, SAS is providing an example template to validate and promote a SAS container through testing and production.
• A ModelOps New Year’s Resolution: Getting Started with SAS Model Manager Ask-the-Expert Webinar -The new year is just around the corner and if you have a new year’s resolution to learn more about ModelOps, then SAS has a FREE webinar for you! Start your new year off right by learning how to use SAS Model Manager on January 5th at 11 a.m. ET. SAS Model Manager helps organizations build a streamlined ModelOps process by connecting data scientists, MLOps engineers and business analysts.
• How Do I Use SAS® Model Manager? Q&A, Slides, and On-Demand Recording - Watch this Ask the Expert session to learn the steps for registering models and modeling metadata from various interfaces, running scoring tests, defining performance monitoring reports, publishing models and much more.
• Preventing unauthorized model publishing: Three ways to better secure models on SAS Viya - In this article, I’ve detailed three ways using SAS Viya 4 to prevent unauthorized model publishing.
• Quickly save python environment requirements and more: the latest release of SASCTL - With the v1.8 release of SASCTL, data scientists can quickly save environment requirements for their Python models as well as select which project version their model is registered to.
• Build Something Awesome: Registration for SAS Hackathon is Open - Are you a data scientist, developer, student, business, or technology partner who is ready to solve a humanitarian or business problem? Do you have a great idea but lack a team or the tools needed to transform your idea to reality? Then the SAS Hackathon is for you!
• Get Hands-on With ModelOps - Are you ready to get hands-on with ModelOps? We’re offering a free trial of SAS® Viya® so you can explore the capabilities. Discover how to apply it – deploy your own models and test scenarios.
• SAS Explore Idea Submissions Open for ModelOps - Call for content is now open for SAS Explore!
• Innovation Abounds in ModelOps at SAS Hackathon - Nearly half the teams who finished their hack leveraged SAS’s ModelOps capabilities. Let’s dive into a few those teams.
• Scoring using Models Deployed into MAS over REST API: A Step-by-Step Guide - Deploying and scoring a model using MAS just takes a few steps. In this article, I will walk through the steps as well as provide some of my best-practices.
• Tag, You’re It! Tagging and Binning Enhancements in SAS Model Manager - You’ve asked and we’ve responded. With the release of SAS Model Manager on SAS Viya 2023.06, we’ve added several new features based on user feedback. -Scoring using Models Deployed into CAS over REST API: A Step-by-Step Guide - In this article, we will focus on using SAS Cloud Analytic Services (CAS) for batch. -Score Millions of Records in Minutes Using Python Models: Python Container Scoring Optimization - The SAS Model Manager team was challenged to run Python models faster. At minimum, the team was tasked with scoring 10 million records using a Python model in 45 minutes. I am happy to report that the SAS Model Manager team not only met that challenge but exceeded it!
• MLOps for Pirates: R-sasctl - R-sasctl was released in January of this year and in our SAS Model Manager community. R-sasctl helps generate the score code and metadata for R models as well as allows R developers to import their model into SAS Model Manager directly from their R development environment.
• Go with the Flow: New SAS Studio Step for Model Registration - We’ve been working with the SAS Studio team to create a new step to register SAS models.
• The Models Text Door: Monitoring NLP models within SAS Model Manager - This means that SAS text models can be managed and deployed with ease. But, what about performance monitoring?
• MLOps Uncoiled: Python’s Path on SAS Viya with SAS Model Manager -Python-sasctl is an open-source package provided by SAS to ease the handoff between the data scientists developing the model and the MLOps engineers managing and deploying the model. Through Python-sasctl, data scientists can take their models developed using Python packages, such as sklearn or xgboost, and automatically generate the scoring code, model pickle file, input variables metadata, output variables metadata, package and version requirements, training performance metadata, and model properties in just a few lines of code. Next, these files are directly pushed into SAS Model Manager.
• Assess Bias of Python Models, Automatically Create Scoring Code for TensorFlow Keras Models and More - We’ve release three new capabilities in the v1.10.0 release of python-sasctl, including a new function to assess model bias, Key Performance Indicator (KPI) and Hyperparameter integration, and support for automatic score code and metadata generation for TensorFlow Keras models.
• Here we Flow Again: New SAS Studio Step for Python Model Registration - Last year, we released a step to register SAS Models and with 2024.01, we now have a step for Python models too!
• Versioning in SAS Model Manager - For today’s article, let’s review how to use project and model versioning as well as discuss recent enhancements to versioning.
• Enforce Responsible AI Best Practices: Trustworthy AI Life Cycle Workflow Available - SAS has just released an experimental version of our Trustworthy AI Life Cycle Workflow for use with SAS® Model Manager and SAS® Workflow Manager on SAS Viya 2024.01 and later.
• Must See ModelOps Sessions & Demos at SAS Innovate - Let’s dive into all things ModelOps at SAS Innovate. In this article, we will go over the sessions, demos, and events where you can learn how to improve outcomes through better analytically driven decisions and model management.
• Model Execution in SAS Model Manager - Today’s article will focus on the three most popular execution engines within SAS Model Manager with tips for administrators for addressing common issues.
• What Problem Will You Solve? Register for the SAS Hackathon Now! - Registration for the SAS Hackathon is open! Everyone needs to know about the SAS Hackathon because this is your opportunity to solve a real-world problem.
• Using Azure OpenAI GPT Models in SAS Viya - Azure OpenAI Service provides access to Large Language Models (LLMs) through REST API, their Python SDK, and a web-based interface in the Azure OpenAI studio. Various models are available out of the box, including GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, and Embeddings model series. You can read more about Azure OpenAI Service here. To incorporate these models in SAS Intelligent Decisioning and SAS Model Manager, we just need to create a Python function and scoring code calling the REST API of a model deployed in Azure OpenAI.
• Scoring using Models Deployed into CAS using SAS Code: A Step-by-Step Guide - In this article, we'll review the steps required to deploy a model into CAS and score new data using that model in SAS code.
• Creating a Model Nutrition Label: Model Cards for SAS Model Studio Models - The first release of the model card supports classification and prediction models from various sources. As users and teams develop and manage their model within SAS Viya, more of the model card will automatically populate. The model card brings together information about the training data, model performance at the time of training, and model performance over time. So, for this article, we will review how to generate a complete model card for models from SAS’s no-code / low-code interface, SAS Model Studio.

GitHub Notebooks and Code

• Black Friday Customer Analysis - Today I want perform a quick customer analysis to practice creating customer clusters as well as building a model to predict customer spending. I will be using several analytic techniques such K-Means clustering, Pipelining, Train-Test split, Lasso modeling, Ridge modeling, Elastic Net modeling, Random Forest modeling, and Gradient Boosting modeling.
• The Data Science Pilot Action Set - The dataSciencePilot action set consists of actions that implement a policy-based, configurable, and scalable approach to automating data science workflows. This action set can be used to automate an end-to-end workflow or to automate steps in the workflow such as data preparation, feature preprocessing, feature engineering, feature selection, and hyperparameter tuning.
• Portfolio Optimization using sasoptpy - In this notebook, we will be using sasoptpy to optimize a potential stock portfolio. The sasoptpy package is a Python package providing a modeling interface for SAS Viya Optimization solvers and supports Linear Problems (LP), Mixed Integer Linear Problems (MILP), Non-Linear Problems (NLP), and Quadratic Problems (QP).
• SASPy for Machine Learning - The SASPy package creates a bridge between Python and SAS 9.4. This package enables a Python developer, familiar with Pandas data frames or SAS datasets, to leverage the power of SAS by connecting a Python process to a SAS 9.4 installation, where it will run SAS code.
• SAS and R Integration for Machine Learning - This notebook represents an example of how you can use SAS Viya with R for analysis.In this example, we will import R packages, start a CAS Session, load data from the local file system into CAS, explore the data, impute missing values, create several models in R, create several models in CAS, score a test set using our models, and assess model performance.
• Viya 2020 Modeling in Python and SAS AutoML - In this simple example, we will build an automated machine learning model using SAS Data Science Pilot via the SWAT package and a Python model pipeline using xgboost and push both models into model manager using the sasctl and pzmm package.
• Custom Fraud Ops KPI - This example creates custom performance monitoring KPIs in SAS Model Manager for fraud operations.
• Leveraging MLflow with SASCTL and Model Manager for SKLearn - In this notebook, we will push a model generated in MLflow into the Model Manager registry.
• Using GIT with SASCLT/PZMM - In this notebook, we will walk through how to leverage SASCTL/PZMM to move assets from SAS Model Manager to a Git Repository.

SAS Viya Highlights Release Show

• Containerized SAS Models in GCP I SAS Viya 2021.2.3 -Product Manager Sophia Rowland will teach you all about containers and show you how to deploy and validate containerized SAS models into Google Cloud Platform (GCP) in just a few clicks in this month’s Release Talk!

Certifications

• SAS Certified Base Programmer for SAS 9, March 2019
• SAS Certified Specialist: Machine Learning Using SAS Viya 3.4, August 2019
• SAS® Certified Specialist: Forecasting and Optimization Using SAS® Viya® 3.4, May 2020
• Microsoft Certified: Azure Data Scientist Associate, October 2020 - October 2023
• SAS® Certified Specialist: Natural Language Processing and Computer Vision Using SAS® Viya® 3.5, November 2020
• SAS® Certified Professional: AI & Machine Learning, November 2020
• Microsoft Certified: Azure Fundamentals, March 2021
• SAS Certified Specialist: ModelOps, September 2022