Using machine learning to improve dialog flow in conversational applications
The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.
Data
Our take on the ideas, information, and tools that make data work.
The future of data warehousing
Executives from Cloudera and PNC Bank look at the challenges posed by data-hungry organizations.
Preserving privacy and security in machine learning
Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services.
The missing piece
Cassie Kozyrkov explores why businesses fail at machine learning despite its tremendous potential and excitement.
Highlights from the Strata Data Conference in New York 2018
Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more.
Progress for big data in Kubernetes
It has become much more feasible to run high-performance data platforms directly inside Kubernetes.
Building accessible tools for large-scale computation and machine learning
The O’Reilly Data Show Podcast: Eric Jonas on Pywren, scientific computation, and machine learning.
Sea change: What happens when Jupyter becomes pervasive at a university?
Fernando Perez talks about UC Berkeley's transition into an environment where many undergraduates use Jupyter and the open data ecosystem as naturally as they use email.
Democratizing data
Tracy Teal explains how to bring people to data and empower them to address their questions.
The future of data-driven discovery in the cloud
Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud.
Jupyter notebooks and the intersection of data science and data engineering
David Schaaf explains how data science and data engineering can work together to deliver results to decision makers.
Data science as a catalyst for scientific discovery
Michelle Gill discusses how data science methods and tools can link information from different scientific fields and accelerate discovery.
Highlights from JupyterCon in New York 2018
Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018.
The reporter’s notebook
Mark Hansen explains how computation has forever changed the practice of journalism.
Why contribute to open source?
Julia Meinwald outlines effective ways to support the unseen labor maintaining a healthy open source ecosystem.
All the cool kids are doing it, maybe we should too? Jupyter, gravitational waves, and the LIGO and Virgo Scientific Collaborations
Will Farr offers lessons about the many advantages and few disadvantages of using Jupyter for global scientific collaborations.
Jupyter trends in 2018
Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018.
Sustaining wonder: Jupyter and the knowledge commons
Carol Willing shows how Jupyter's challenges can be addressed by embracing complexity and trusting others.
Simplifying machine learning lifecycle management
The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.
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.
How privacy-preserving techniques can lead to more robust machine learning models
The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.
Data’s day of reckoning
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
The five Cs
Five framing guidelines to help you think about building data products.
How to take machine learning from exploration to implementation
Recognizing the interest in ML, the Strata Data Conference program is designed to help companies adopt ML across large sections of their existing operations.