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Data science platforms News
April 14, 2021
Despite their desire to use data science in their decision-making process, some organizations can't find qualified data scientists to develop and run their data science initiatives.
November 17, 2020
Amid the pandemic, Boston-based AI vendor DataRobot raised $270 million in a pre-IPO fundraising round. The company is likely to file for an IPO.
July 24, 2020
AI adoption has appeared to grow this year, as organizations deploy automation to augment dwindling workforces and help deal with growing demand during the COVID-19 pandemic.
July 09, 2020
DotData opens the doors to new markets with DotData Stream, a new product that enables customers to create low-memory, low-latency models at the edge.
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Crypto-agility, or cryptographic agility, is a data encryption practice used by organizations to ensure a rapid response to a cryptographic threat. Continue Reading
Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest. Continue Reading
Before AI can revolutionize business processes or decision-making, companies need a strong foundation. These tools, platforms and applications help enterprises get started with AI. Continue Reading
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Numerous tools are available for data science applications. Read about 15, including their features, capabilities and uses, to see if they fit your analytics needs. Continue Reading
Data science is the process of using advanced analytics techniques and scientific principles to analyze data and extract valuable information for business decision-making, strategic planning and other uses. Continue Reading
Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights. Continue Reading
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Building a viable, reliable and agile machine learning model that streamlines operations and bolsters business planning takes patience, preparation and perseverance. Continue Reading
Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models. Continue Reading
Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights. Continue Reading
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Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading
Most data science projects end up facing similar problems, such as lack of robustness and data quality issues. In this feature, experts offer tips on how to overcome these challenges. Continue Reading
To create his March Madness bracket predictions, the head of data science at DataRobot uses a host of machine learning algorithms and some predictive analytics. Continue Reading