Gradient Flow

Tag: radar

  • Turning big data into actionable insights

    [A version of this article appears on the O’Reilly Radar.] The O’Reilly Data Show podcast: Evangelos Simoudis on data mining, investing in data startups, and corporate innovation. Subscribe to the O’Reilly Data Show Podcast to explore…

  • We need open and vendor-neutral metadata services

    [A version of this article appears on the O’Reilly Radar.] Comprehensive metadata collection and analysis can pave the way for many interesting applications. As I spoke with friends leading up to Strata + Hadoop World NYC…

  • From search to distributed computing to large-scale information extraction

    Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science. February 2016 marks the 10th anniversary of Hadoop — at a point in time when many IT…

  • Celebrating the real-time processing revival

    [A version of this article appears on the O’Reilly Radar.] Register for Strata + Hadoop World NYC, which will take place September 29 to Oct 1, 2015. A few months ago, I noted the resurgence in…

  • The tensor renaissance in data science

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Anima Anandkumar on tensor decomposition techniques for machine learning. After sitting in on UC Irvine Professor Anima Anandkumar’s Strata + Hadoop…

  • More tools for managing and reproducing complex data projects

    A survey of the landscape shows the types of tools remain the same, but interfaces continue to improve. [A version of this post appears on the O’Reilly Radar.] As data projects become complex and as data…

  • A real-time processing revival

    [A version of this post appears on the O’Reilly Radar blog.] Things are moving fast in the stream processing world. There’s renewed interest in stream processing and analytics. I write this based on some data points…

  • Redefining power distribution using big data

    [A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: Erich Nachbar on testing and deploying open source, distributed computing components. When I first hear of a new open source…

  • Let’s build open source tensor libraries for data science

    [A version of this post appears on the O’Reilly Radar blog.] Tensor methods for machine learning are fast, accurate, and scalable, but we’ll need well-developed libraries. Data scientists frequently find themselves dealing with high-dimensional feature spaces.…

  • Forecasting events, from disease outbreaks to sales to cancer research

    [A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: Kira Radinsky on predicting events using machine learning, NLP, and semantic analysis. Editor’s note: One of the more popular speakers…