Computer vision solutions with privacy-by-design
SAS' Hamza Ghadyali reveals how to build computer vision solutions with privacy baked into their design.
Tag: Analytics R&D
World Creativity & Innovation Week 2022: The Return - Part 2
In the second of two Q&As with R&D colleagues, SAS' Udo Sglavo provides a window into how we approach drug-development challenges with machine learning.
World Creativity and Innovation Week 2022: The Return - Part 1
A note from Udo Sglavo: At SAS, what we deliver to our customers is a product of creative minds thinking differently, challenging the norm, taking risks, and learning from trial and error (The greatest teacher, failure is). For the return of World Creativity & Innovation Week, we want to share
Accuracy versus interpretability? With generalized additive models (GAMs), you can have both
SAS' Brian Gaines provides a primer on GAMs.
Collaborative Filtering and Supervised Learning: A Tale of Two Methods
SAS' Xuejun Liao weighs the pros and cons of collaborative filtering and supervised learning and explores their use in a unified framework.
Computer vision in cancer treatment: assessing lesion therapy response with SAS Visual Data Mining and Machine Learning
SAS' Courtney Ambrozic highlights how to use SAS VDMML to assess lesion response to chemotherapy for patients with colorectal liver cancer.
Image-specific processing functions in computer vision
SAS' Pankaj Telang shows you new image-specific processing capabilities in SAS Visual Data Mining and Machine Learning.
Integrating ONNX models into ASTORE
SAS' Yongqiao Xiao, Maggie Cech and Patrick Koch describe an ONNX model in ASTORE and demonstrate how to use the information to save an ONNX model to an analytic store.
Using the SASEBEA Interface Engine to explore BEA statistical data
SAS' Kelly Fellingham, an advanced analytics software developer, reveals how SAS software's new SASEBEA interface helps you identify patterns in US economics data.
Nobel Prize winners and SAS causal econometrics software
In a Q&A with SAS' Udo Sglavo, Xilong Chen of SAS parses the work of 2021 Nobel Prize winners for Economics.
Introducing JupICL: Fast Image Labelling Entirely Within a Jupyter Notebook
SAS' Hamza Ghadyali introduces you to JupICL, a SAS field-tested, easy-to-use, customizable image labelling tool that runs entirely inside a Jupyter notebook.
Using State Space Models for the Stability Monitoring of Streaming Data
SAS' Rajesh Selukar introduces you to a new scoring feature.
How SAS developed a digital twin of a supply chain
SAS' Bahar Biller, an operations researcher, details how to develop a supply chain digital twin.
Art or science? Choosing the right regression model
SAS' Udo Sglavo and Jan Chvosta discuss the power of a regression framework and choosing the correct regression model.
Which regression technique is appropriate for my data?
SAS' Udo Sglavo interviews colleague Jan Chvosta, director of Scientific Computing at SAS, on regression analysis and how it works.
Causal inference and policy evaluation with deep neural networks
SAS' Xilong Chen introduces the new DEEPCAUSAL procedure in SAS Econometrics for causal inference and policy evaluation and much more.
Automate spatial regression model selection using PROC CSPATIALREG
SAS' Ghohui Wu shows you how to construct spatial weights matrices based on contiguity and distance measures, then shows how PROC CSPATIALREG automates spatial regression model selection.
A million images a minute: a behind-the-scenes look at the loadImages action in SAS Viya
SAS' Fijoy Vadakkumpadan, a computer vision expert, sheds light on how loadImages works in SAS Viya.
Tips for parallel processing in CASL
This is the second post in a series covering parallel processing in SAS Viya. The first post served as an introduction to parallel processing. It covered parallel processing uses in data science and the SAS Viya products that facilitate it. There are countless opportunities for using parallel processing within data
Using simple metrics to improve image matching performance
The computer vision team was recently presented with the following challenge concerning image matching performance. An insurance company has the capability to submit claims and supporting materials digitally via an online interface. They need, however, to be able to detect when images already used in previous claims have been resubmitted
Physical distance monitoring using SAS
Learn how a computer vision application makes it safer, post-pandemic, for employees to return to factories.
Streamline the scoring of multiple analytic stores
Recently, we’ve released a new feature in ASTORE: score with multiple analytic stores. In the process, we may create multiple analytic stores with dependencies among them (the output of some analytic stores is an input to others). This feature streamlines the scoring process of multiple analytic stores. It enables the
Parallel Processing in SAS Viya
Most computers can execute operations in parallel due to their multicore infrastructure. Performing more than one operation simultaneously has the potential to speed up most tasks and has many practical uses within the field of data science. SAS Viya offers several products that facilitate parallel task execution. Many of these
SAS and Microsoft collaborate to democratize the use of Deep Learning Models
Note from Gül Ege Sr. Director, Analytics R&D, IoT: The pattern of training in the Cloud, with your choices of framework and inferencing at the Edge with a target environment, are especially common in Internet of Things (IoT). In IoT, there is a proliferation of hardware environments on the Edge.
The Celebration of World Creativity & Innovation Week with Analytics R&D Continues
In the second of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo interviews Chris Barefoot, Matthew Galati, Courtney Ambrozic and Davood Hajinezhad.
Celebrate World Creativity & Innovation Week with Analytics R&D - Part 1
In the first of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo introduces you to developers Amy Shi, Maggie Du and Phil Helmkamp.
Maximize product quality with Optimization and Machine Learning models
Machine Learning models are becoming widely used to formulate and describe processes’ key metrics across different industry fields. There is also an increasing need for the integration of these Machine Learning (ML) models with other Advanced Analytics methodologies, such as Optimization. Specifically, in the manufacturing industry, SAS explored state-of-the-art science
Automated linearization in SAS Optimization
Linear programming (LP) and mixed integer linear programming (MILP) solvers are powerful tools. Many real-world business problems, including facility location, production planning, job scheduling, and vehicle routing, naturally lead to linear optimization models. Sometimes a model that is not quite linear can be transformed to an equivalent linear model to reduce
Spatial econometric modeling unleashes the geographic potential of your data
Note from Udo Sglavo: In our peace of mind blog series, we documented areas of analytics that are either evolving or not necessarily in the standard toolset of data scientists. We looked at causal modeling, network analytics, and econometrics, to name a few. With this blog post, we would like
Mathematical optimization at SAS
Note from Udo Sglavo on mathematical optimization: When data scientists look at the essence of analytics and wonder about their daily endeavor, it often comes down to supporting better decisions. Peter F. Drucker, the founder of modern management, stated: "Whenever you see a successful business, someone once made a courageous decision."