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 ...

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 ...

Last week’s Informatica World 2016 brought out a lot of talk involving data quality, real-time live data and the automation of ingesting and analyzing data in order to turn it into something ...

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 ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

You often hear that data is the new oil. This valuable, ever-changing commodity has begun to play a starring role in many cloud-native applications. Yet, according to a number of DevOps teams, data ...

2023 was a year marked by innovation and change in the enterprise technology landscape. Companies of all sizes continue to accelerate their digital transformation efforts and leverage artificial ...

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 ...

In an era where rapid software releases, robust infrastructure, and data security are no longer optional, organizations are increasingly embracing cloud-native principles to support their growth. The ...