IBM’s Watson was put in a particularly bright spotlight in 2011 when it faced off against the world’s two most successful game show contestants of all time on Jeopardy. When it came to the Final Jeopardy question, Watson comically gave the response “What is Toronto?????” The category was U.S Cities. Nonetheless, Watson won the match
Co-authored by Joshua Fleischer and Sarah Young. As businesses increase their utilization of data and the insights they gather from it, universities are realizing the need to prepare students for today’s data-driven climate. Professors toe an interesting line. In a semester’s time, they need to provide a basic academic background for data modeling and analytics
According to Gartner, only 53% of machine learning models convert from prototype to production. The statement above can also be articulated like this: 47% of machine learning projects fail to go into production. If an organization is set out to implement a machine learning solution in the first place, there can be a big chance
Data science is certainly at the top of many business leaders’ minds at the moment. It seems that almost daily, headlines announce things like the increase in the number of firms that are investing in AI and analytics, or about the trillions of dollars in “business value” that experts say will be created in the next decade
My dad was a foundry equipment salesman. He spent his adult life driving to small towns across the Midwest and meeting with foundry owners. He conducted his business with instinct and trust building: with a lot of sharing goals and plans, discussing pricing and features, and handshakes. What my dad did for a living was
When it comes to helping hiring managers and job seekers find the best option that fits their needs, no recruiting agency is as well-known and trusted as Robert Half. The company pioneered the idea of professional staffing services more than 70 years ago. And Robert Half continues to innovate today. By harnessing the power of
Nowadays, the use of data in the field of technology is growing exponentially. Lots of companies are working day and night on them. But the question is why do we need data classification? First we have to understand what data classification is. Data classification is a process of predicting the class (labels/categories) of the given
By now, it’s widely regarded that the “big data” era is over and we’re now solidly in the era of cloud data. Once considered the pinnacles of tech, systems like Hadoop, Apache Spark, and others are now seen as legacy systems. They require a lot of resources to maintain and scaling generally takes advanced planning.
Last month, a most unusual AWS re:Invent came to an end. This year featured none of the crowds, ultra-marathon walks, or nightlife we have come to expect from years prior. It was, however, jam-packed with information on new services being offered by AWS. At Atrium, our relationship with AWS has always been, and will likely
As the market continues to react and recover from the COVID pandemic, we are already seeing AI use cases surface. In the months to come, there will be a variety of additional opportunities for medical device companies to improve their growth and margin in a highly competitive and evolving landscape — and to see measurable