[A version of this post appears on the O’Reilly Radar.] Ray is beginning to be used to power large-scale, real-time AI applications. Machine learning adoption is accelerating due to the growing number of large labeled data sets, languages aimed at data scientists (R, Julia, Python), frameworks (scikit-learn, PyTorch, TensorFlow, etc.), and tools for building infrastructure toContinue reading “Notes from the first Ray meetup”
Author Archives: Ben Lorica
5 findings from O’Reilly’s machine learning adoption survey companies should know
New survey results highlight the ways organizations are handling machine learning’s move to the mainstream. By Ben Lorica and Paco Nathan. [This post originally appeared on the O’Reilly Radar.] As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work.Continue reading “5 findings from O’Reilly’s machine learning adoption survey companies should know”
How privacy-preserving techniques can lead to more robust machine learning models
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning. In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early toolsContinue reading “How privacy-preserving techniques can lead to more robust machine learning models”
Specialized hardware for deep learning will unleash innovation
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture. In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specializedContinue reading “Specialized hardware for deep learning will unleash innovation”
Data collection and data markets in the age of privacy and machine learning
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools focused on data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the dataContinue reading “Data collection and data markets in the age of privacy and machine learning”
What machine learning means for software development
[A version of this post appears on the O’Reilly Radar.] “Human in the loop” software development will be a big part of the future. By Ben Lorica and Mike Loukides Machine learning is poised to change the nature of software development in fundamental ways, perhaps for the first time since the invention of FORTRAN andContinue reading “What machine learning means for software development”
Data regulations and privacy discussions are still in the early stages
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy. In this episode of the Data Show, I spoke with Aurélie Pols of Mind Your Privacy, one of my go-to resources when it comes to data privacy and data ethics. This interview tookContinue reading “Data regulations and privacy discussions are still in the early stages”
Managing risk in machine learning models
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain. In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta.Continue reading “Managing risk in machine learning models”
Understanding automation
An overview and framework, including tools that can be used to enable automation. In this post, I share slides and notes from a talk Roger Chen and I gave in May 2018 at the Artificial Intelligence Conference in New York. Most companies are beginning to explore how to use machine learning and AI, and we wantedContinue reading “Understanding automation”
The real value of data requires a holistic view of the end-to-end data pipeline
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications. In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science andContinue reading “The real value of data requires a holistic view of the end-to-end data pipeline”