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Ever wonder why so many machine learning (ML) models never see the light of day? Despite their huge potential, only 32% of data scientists say their models usually get deployed. Even more shocking, 43% report that 80% or more of their models never make it into production. This means many businesses miss out on the full value of their AI projects. Imagine a retail company spending months developing a sophisticated customer recommendation system, only for it to never be implemented—losing out on p

Healthcare providers manage an enormous volume of data daily, approximately 137 terabytes, most of which is unstructured. This includes a wide array of formats such as medical images, clinical notes, and genetic test results. Unstructured data, crucial for patient care, poses significant challenges due to its complexity and the varied sizes of its components. The volume of healthcare data is rapidly increasing, fueled by the widespread adoption of electronic health records and advances in medica

Since 2015, Sciforce has been an active contributor to the OHDSI scientific community. Our medical team is consistently at the forefront of OHDSI events, sharing groundbreaking research and driving advancements in health data harmonization that empower better health decisions and elevate care standards. The fall event was no exception: from October 20 to 22 Polina Talapova, Denys Kaduk, and Lucy Kadets were delegated as our company representatives to the Global OHDSI Symposium with more than 440

Each nation has its ancient monument or a pagan holiday — the relic of the days when our ancestors tried to persuade their gods to give them more rain, no rain, better harvest, fewer wars and many other things considered essential for survival. However, neither Stonehenge nor jumping over fire could predict the gods’ reaction. It was a totally reactive world with no forecast. However, as time passed, people started to look into the future more inquisitively trying to understand what would be wai

If we think of the most successful and widespread applications of machine learning in business, one of the examples would be recommender systems. Each time you visit Amazon or Netflix, you see recommended items or movies that you might like — the product of recommender systems incorporated by these companies. Though a recommender system is a rather simple algorithm that discovers patterns in a dataset, rates items and shows the user the items that they might rate highly, they have the power to b

It is quite often that in our blog we explore intricate connections between state-of-the-art technologies, or explore the mesmerizing depth of a new technique. However, AI or data science is not only bragging about new exciting methods that boost accuracy by 2% (which is a big gain) but about making data and technology work for you. It will help you increase sales, understand your customers, predict future faults in process lines, make an insightful presentation, submit a term project, or have a

The new world we live in gives us more help — and more doubts. Machines stand behind everything — and the scope of this everything is only growing. To what extent can we trust such machines? We are used to relying on them in market trends, traffic management, and maybe even in healthcare. Machines are now analysts, medical assistants, secretaries, and teachers. Are they reliable enough to work as HRs? Psychologists? What can they tell about us? Let’s see how text analysis can analyze your soft s

By now you have already gained enough knowledge and skills about Data Science and have built your first (or even your second and third) project. At this point, it is time to improve your workflow to facilitate further development process. There is no specific template for solving any data science problem (otherwise you’d see it in the first textbook you come across). Each new dataset and each new problem will lead to a different roadmap. However, there are similar high-level steps in many differ