Gradient Flow

Category: Data Engineer

  • How to build analytic products in an age when data privacy has become critical

    Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products. In this post, I share slides and notes from a talk I…

  • What machine learning engineers need to know

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar. In this episode of the Data…

  • We need to build machine learning tools to augment machine learning engineers

    We need to build machine learning tools to augment our machine learning engineers. In this post, I share slides and notes from a talk I gave in December 2017 at the Strata Data Conference in Singapore…

  • What lies ahead for data in 2018

    [A version of this post appears on the O’Reilly Radar.] How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape data in 2018. 1. New tools will make graphs and time…

  • How machine learning will accelerate data management systems

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures. In this episode of the Data…

  • The current state of Apache Kafka

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Neha Narkhede on data integration, microservices, and Kafka’s roadmap. In this episode of the Data Show, I spoke with Neha Narkhede,…

  • Building a natural language processing library for Apache Spark

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production.…

  • 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…

  • How Ray makes continuous learning accessible and easy to scale

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications. Subscribe to the O’Reilly Data Show…

  • A scalable time-series database that supports SQL

    [A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and…