Marinela Profi is a Product Strategy Lead for Artificial Intelligence solutions at SAS, across the areas of market engagement, strategy, messaging, content and product readiness. Over the past 6 years, she also worked as a data scientist, analyzing data and developing AI models, to drive AI implementation within the following industries: Banking, Manufacturing, Insurance, Government and Energy. Marinela has a Bachelor’s in Econometrics, a Master of Science in Statistics, and Master’s in Business Administration (MBA). Marinela enjoys sharing her journey on LinkedIn, and on the main stage, to help those interested in a career in data and tech.
Data quality is a cornerstone for integrating large language models (LLMs) into organizations. The adage "garbage in, garbage out" holds particularly true here. High-quality data is the lifeblood that ensures the accuracy, relevance, and reliability of the model's outputs. In a business context, this translates to insights and decisions that
While large language models (LLMs) have become synonymous with advanced AI capabilities, their integration into various business and technological domains is often accompanied by significant costs. These costs arise from the extensive computational resources required for training and running these models. However, traditional natural language processing (NLP) techniques offer a
Most people associate generative AI (GenAI) with large language models (LLMs). While LLMs focus specifically on generating text, GenAI encompasses a wider range of content generation tasks beyond just language, including images, music and more. Broadly speaking, GenAI uses machine learning algorithms to analyze and learn from existing data sets
Large language models (LLMs) are at the forefront of today’s AI, not merely as technological marvels but as transformative agents reshaping how businesses innovate, operate and deliver value. Think of them as the wizards of words, capable of understanding language and transforming it in ways that benefit organizations. However, as
SAS' Marinela Profi and Sophia Rowland elaborate on IDC including SAS among the leading platform providers for Machine Learning Operations.
A cancer journey affects both physical and mental health. This often results in feelings of social isolation, loss of identity, clinical depression and even PTSD. This often goes unrecognized and undiagnosed due in part to lack of resources, tools and time. Swedish startup War On Cancer wondered whether they could
[Editor's note: this post was co-authored by Marinela Profi and Wilbram Hazejager] Data science teams are multidisciplinary, each with different skills and technologies of choice. Some of them use SAS, others may have analytical assets already built in Python or R. Let's just say each team is unique. As part
Turn analytical models into business value and smarter decisions with this special collection of papers about SAS Model Management. Without a structured and standardized process to integrate and coordinate all the different pieces of the model life cycle, a business can experience increased costs and missed opportunities. SAS Model Management solutions enable organizations to register, test, deploy, monitor, and retrain analytical models, leveraging any available technology – including open-source models in Python, R, and TensorFlow –into a competitive advantage.
