Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key Data scientists have some practices and needs in ...

As system architecture moves to the cloud, understanding the impacts of new code releases and points of failure requires a whole new approach. The increase in agility, bandwidth, and security has been ...

It may be a stretch to call data science commonplace, but the question “what’s next” is often heard with regard to analytics. And then the conversation often turns straight to Artificial Intelligence ...

For data scientists, creating a perfect statistical model is all for naught if the compute power required is prohibitive. We need tools to assess the performance impacts of modeling alternatives Big ...

While the general advancement of enterprise software is often thought of as the marrying of software development and data stacks, a third consideration is essential to driving tangible advancement. It ...

Data science and machine learning are often associated with mathematics, statistics, algorithms and data wrangling. While these skills are core to the success of implementing machine learning in an ...

Delphix offers a data anti-gravity platform to help developers and data analysts accelerate DevOps, data science projects without long waits for test data Software development cycles are speeding up — ...

iRobot has used its new design, software, and data science strategies to expand into new areas, using an approach to the smart home that is different from its big tech rivals. This download provides ...

7 big data goals for 2021: AI, DevOps, hybrid cloud, and more Your email has been sent Image: iStockphoto/metamorworks Must-read big data coverage What Powers Your ...