SAS' Fijoy Vadakkumpadan, a computer vision expert, sheds light on how loadImages works in SAS Viya.
Tag: Analytics R&D
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
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
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
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
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.
In the second of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo interviews Chris Barefoot, Matthew Galati, Courtney Ambrozic and Davood Hajinezhad.
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.
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
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
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
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."
A note from Udo Sglavo: When people ask me what makes SAS unique in the area of analytics, I will mention the breadth of our analytic portfolio at some stage. In this blog series, we looked at several essential components of our analytical ecosystem already. It is about time to
A note from Udo Sglavo: This post offers an introduction to complex optimization problems and the sophisticated algorithms SAS provides to solve them. In previous posts of this series, we learned that data availability, combined with more and cheaper computing power, creates an essential opportunity for decision-makers. After looking at network analytics
A note from Udo Sglavo: A wealth of connectivity is pervasive in the data we gather across many industries. In other words, networks are all around us. A data science trend you cannot ignore is to organize, learn from, and drive decision-making based on connected data. Network analytics engines provide efficient
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in the cloud, or at the edges of the network – analytics needs to be there with it. Being true to this principle means we
“Technology is an industry that eats its young, it is rare to come across providers that have been around for more than a human generation.” Tony Bear, Big on Data With more than 40 years in the market, SAS is one of the rare technology providers that has been around
A note from Udo Sglavo: The need for randomization in experimental design was introduced by the statistician R. A. Fisher in 1925, in his book Statistical Methods for Research Workers. You would assume that developing a successful treatment for COVID-19, the illness caused by the SARS-CoV-2 virus, will eventually conclude in
A note from Udo Sglavo: In Digital transformation, scientific computing, and peace of mind, I mention that the COVID-19 pandemic is paralyzing the world. However, new challenges are also inspiring new ideas to tackle those challenges. We might ask questions about what is causal in nature, trying to figure out
Remember Subconscious Musings? It was the name of the blog Radhika Kulkarni (now retired Vice President of SAS R&D) started in 2012. She wrote about trends that drove innovation and challenges that expanded the boundaries of what we thought was possible. It eventually evolved into what we now know as