Medical imaging is an important tool for the diagnosis and localization of disease. Over the past decade, collections of medical images have grown rapidly,...

This is the second post in a two-part series in which I propose a practical guide for organizations so you can assess the quality of text summarization models for your domain. For an introduction to text summarization, an overview of this tutorial, and the steps to create a baseline for our project (also referred to […]

When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested in technology but usually don’t care much about […]

Machine learning (ML) helps organizations increase revenue, drive business growth, and reduce cost by optimizing core business functions across multiple verticals, such as demand forecasting, credit scoring, pricing, predicting customer churn, identifying next best offers, predicting late shipments, and improving manufacturing quality. Traditional ML development cycles take months and require scarce data science and ML […]

Data lakes have become the norm in the industry for storing critical business data. The primary rationale for a data lake is to land all types of data, from raw data to preprocessed and postprocessed data, and may include both structured and unstructured data formats. Having a centralized data store for all types of data […]

The Background of Data Science Roles It was thought a few years ago that 2018 would amount a huge demand-supply gap in the Data Science market as supply would fail to keep pace with the rising demand for expert data scientists. However, the buzz from Gartner, which said more than 40 percent of Data Science […]

The post Data Scientist vs. Data Engineer appeared first on DATAVERSITY.