Gradient Flow

Category: Data Science

  • How companies can navigate the age of machine learning

    To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models. Over the last few years, the data community has focused on gathering and collecting data, building infrastructure…

  • Transforming organizations through analytics centers of excellence

    [A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: Carme Artigas on helping enterprises transform themselves with big data tools and technologies. In this episode of the Data Show,…

  • The state of machine learning in Apache Spark

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Ion Stoica and Matei Zaharia explore the rich ecosystem of analytic tools around Apache Spark. In this episode of the Data…

  • The current state of applied data science

    [A version of this post appears on the O’Reilly Radar.] Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning. As we enter the latter part of 2017, it’s time to…

  • A framework for building and evaluating data products

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches. Subscribe to the O’Reilly Data…

  • Programming collective intelligence for financial trading

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models. Subscribe to the O’Reilly Data Show Podcast to explore…

  • Creating large training data sets quickly

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data. Subscribe to the O’Reilly Data Show Podcast to…

  • What are machine learning engineers?

    [A version of this appears on the O’Reilly Radar.] A new role focused on creating data products and making data science work in production. by Ben Lorica and Mike Loukides We’ve been talking about data science…

  • Data science and deep learning in retail

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products. Subscribe to the O’Reilly Data Show Podcast…

  • Data preparation in the age of deep learning

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets. Subscribe to the O’Reilly Data Show Podcast…