Subject archive for "perspective," page 4

It is finally time to kill the expensive, dangerous myth of the “Citizen Data Scientist” and shift attention to a neglected, real-life person that is already critical to your model-driven business today, and who will be even more important in the future – the “honorary” or part-time data scientist.

When it comes to scaling your AI capabilities, you need more graphics processing units (GPUs). They’re the fastest and most cost-effective way to train your deep learning models that power your AI applications. The parallel processing power of GPUs boosts performance for AI use cases ranging from natural language understanding (NLU) – such as speech recognition, text analytics, and virtual agents – to computer vision – such as defect detection, object recognition, and facial analysis. Indeed, they are critical for nearly every AI application built on unstructured and semi-structured data.

Pi Day is upon us and, while we celebrate “Pi” with pie, we mustn’t forget that other great “Pi” in our lives, which is, of course, Python! That programming language which has emerged as the de facto standard for production-grade data science, used by the majority of production-grade data scientists.

Today’s businesses are investing heavily in data science – spending on software, hardware and services is projected to break the $500 billion mark by 2024, according to IDC. Data science models with machine learning (ML) and artificial intelligence (AI) techniques have proven their worth at forging new revenue streams and upending entire industries. For a model-driven business, new revenue from such use cases can range from hundreds of millions to billions of dollars. At successful companies, leaders have built a well-oiled analytical flywheel to create a steady flow of models that can tap this new gold rush.

When medical science meets data science, amazing things can happen.