One simple chart: TensorFlow vs. PyTorch in job postings

In a post from last summer, I noted how rapidly PyTorch was gaining users in the machine learning research community. At that time PyTorch was growing 194% year-over-year (compared to a 23% growth rate for TensorFlow). That post used research papers, specifically simple full-text searches of papers posted on the popular e-print service arXiv.org.

Notes from the second PyTorch Developer conference

I was able to catch the morning sessions of the PyTorch developer conference in San Francisco, and just like last year the event was well-organized and packed. The official blog post has details on the many announcements  pertaining to the version 1.3 release, but here are a few that caught my eye: Growth: The scrolling listContinue reading “Notes from the second PyTorch Developer conference”

Machine learning for operational analytics and business intelligence

The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts. In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a startup that is using machine learning to improve operational analytics. Bailis is also an assistant professor of computer scienceContinue reading “Machine learning for operational analytics and business intelligence”

Machine learning and analytics for time series data

The O’Reilly Data Show Podcast: Arun Kejariwal and Ira Cohen on building large-scale, real-time solutions for anomaly detection and forecasting. In this episode of the Data Show, I speak with Arun Kejariwalof Facebook and Ira Cohenof Anodot(full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, whereContinue reading “Machine learning and analytics for time series data”

How New Tools In Data And AI Are Being Used In Healthcare And Medicine

An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data. By Ben Lorica and Mike Loukides. [A version of this post appears on the O’Reilly Radar.] AI will have a huge impact on healthcare. It is currently moving out of the laboratory and into real-world applications for healthcareContinue reading “How New Tools In Data And AI Are Being Used In Healthcare And Medicine”

Labeling, transforming, and structuring training data sets for machine learning

The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel. In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a companyContinue reading “Labeling, transforming, and structuring training data sets for machine learning”

Make data science more useful

The O’Reilly Data Show Podcast: Cassie Kozyrkov on connecting data and AI to business. In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes “decision intelligence” as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science withContinue reading “Make data science more useful”

You’ll want Nexar’s newly released Live Map for your city

Extracting and exposing valuable insights to enable smart cities and many other applications. I recently had the privilege of getting a preview of Nexar’s Live Map, from my friend, Nexar’s CTO and co-founder Bruno Fernandez-Ruiz. Nexar uses off-the-shelf smartphones and dash-cams, sophisticated data ingestion, data processing, sensor fusion, and machine learning software to realize theirContinue reading “You’ll want Nexar’s newly released Live Map for your city”

Managing machine learning in the enterprise: Lessons from banking and health care

A look at how guidelines from regulated industries can help shape your ML strategy. By Ben Lorica, Harish Doddi, David Talby. As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. In recent posts,Continue reading “Managing machine learning in the enterprise: Lessons from banking and health care”