Subject archive for "perspective," page 3


Rocketing Confidence in Data Science, Poll Finds: Are Better Tools the Reason?

Businesses are increasingly betting big on data science for ambitious near-term growth, just one more indication that the rapidly rising profession is making itself a huge force for innovation in fields as diverse as healthcare & pharma, defense, insurance, and financial services. Nearly half of respondents in a recent poll said that their company’s leadership expects data science efforts to produce double-digit revenue growth. A similar survey in 2021 put that same figure at only 25%, indicating growing expectations for the young profession.

By Lisa Stapleton4 min read

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The Case for Reproducible Data Science

Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method. In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed since the original project.

By Sundeep Teki8 min read


Why Hybrid Cloud is the Next Frontier for Scaling Enterprise Data Science

An exciting new trend is rising in enterprise data science, and it’s breaking down the silos between on-premises and cloud environments to unlock the benefits of each, all while improving collaboration and regulatory compliance. Advanced, model-driven companies—especially the ones that are out-innovating their competitors with machine learning and AI—are adopting hybrid cloud strategies for their data science initiatives. The most advanced are even repatriating data science workloads back on-premises, while simultaneously exploiting the flexibility of multiple cloud environments.

By Kjell Carlsson9 min read


How data science can fail faster to leap ahead

One of the biggest challenges in data science today is finding the right tool to get the job done. The rapid change in best-in-class options makes this especially challenging - just look at how quickly R has fallen out of favor while new languages pop up. If data science is to advance as rapidly as possible in the enterprise, scientists need the tools to run multiple experiments quickly, discard approaches that aren’t working, and iterate on the best remaining options. Data scientists need a workspace where they can easily experiment, fail quickly, and determine the best data solution before they run a model through certification and deployment.

By Nikolay Manchev8 min read


Tackle The Three Rs of Trustworthy AI for Ethical, Legal And Reliable Models

By Kjell Carlsson, Head of Data Science Strategy & Evangelism at Domino on April 27, 2022 in Perspective

By Kjell Carlsson8 min read

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