While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future. As we
A sneak-peek into a few AI trends we picked for you from Data Natives 2019 – Europe’s coolest Data Science gathering. We are about to enter 2020, a new decade in which Artificial Intelligence is expected to dominate almost all aspects of our lives- the way we live, the way
Data Science is described as “the career of the future,” but finding Data Scientists for your company could be a major challenge. Here is how you could find one for your company. As demand keeps growing for people with the expertise to manage, analyze and safely store ever-larger sets of
An aspiration to create a data-driven future has resulted in massive data lakes, where even the most experienced data scientists can drown in. Today, it’s all about what you do with that data that determines your success. And IBM has the recipe for this. Read on. “Without data, you simply can’t
Here is a look at an AI startup that raised $44.3 million in venture capital funding and built a product that has a vision to not only scheduling a “time” for meetings but also take care of every little detail that comes along. Find out how intelligent these AI assistants
By leveraging Data Science, AI, and other digital technologies, the healthcare industry could build complementary health solutions that are personalized to the specific needs of patients. Here is how and why. The world population grows by more than 80 million per year, according to a 2017 report by the United Nations.
Aleksandar Kovačević, Sales Engineer at InterSystems, shares how companies use MLOps combined with a central multi-model database to get the most out of their machine learning initiatives. Artificial Intelligence (AI) and Machine Learning (ML) are hot topics at the moment. But when it comes to producing quantifiable results, there is
If you’re a Data Scientist, you’ve likely spent months earnestly developing and then deploying a single predictive model. The truth is that once your model is built – that’s only half the battle won. A quarter of a Data Scientist’s working life often goes something like this: You met with
A recent survey of over 225 enterprise Data Scientists, AI technologists and business stakeholders involved in active AI and machine learning (ML) projects, suggests that for most organizations, it’s still early days for AI technology. The AI market is projected to become a $190 billion industry by 2025 ( according
With a saturated analytics and business intelligence (A&BI) market, why are we still struggling to make analytics platforms work for Data Scientists? And perhaps more importantly, why are we failing to see a return on our expensive Data Science initiatives? It’s not for a lack of effort, a lack of