Marinela Profi is the Global AI & GenAI Lead at SAS. Leveraging her extensive background in data science, Marinela brings a unique perspective that bridges the realms of technology and marketing. She drives AI implementation within Banking, Manufacturing, Insurance, Government and Energy sectors. Marinela has a Bachelor’s in Econometrics, a Master of Science in Statistics and Machine Learning and Master’s in Business Administration (MBA). She enjoys sharing her journey on LinkedIn, and on the main stage, to help those interested in a career in data and tech.
Fearmongers would have you believe that AI will replace us, but I truly believe there has never been a better time in history to be a developer. With the advance of artificial intelligence into generative AI (GenAI) and enhanced computing power, we stand on the brink of a new era
Remember the first time you held a smartphone in your hand? It wasn’t just a fancy phone – it was the beginning of a revolution. Before smartphones, we used separate calls, cameras, emails, and navigation devices. But almost overnight, this gadget transformed everything: how we communicate, consume media, work and
Before rushing to invest in generative AI (GenAI), organizations must pause and take a step back. GenAI is powerful and has shown potential to revolutionize multiple industries – but it’s not a silver bullet. Now that we’ve finally gotten past the hype phase, it’s time to look at the realities
With all the technology changes coming in the next five years, what should organizations invest in first? The innovations keep coming and so do the 3 a.m. night sweats for decision makers. “How will we catch up when technology seems to change overnight, nearly every night?” It’s a surprisingly common
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.
