Paco Nathan
Paco Nathan is a Principal DevRel Engineer at Senzing, focused on entity resolution for knowledge graph practices.
Radar
Three surveys of AI adoption reveal key advice from more mature practices
An overview of emerging trends, known hurdles, and best practices in artificial intelligence.
How companies are building sustainable AI and ML initiatives
A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts.
Jupyter trends in 2018
Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018.
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.
Content
What to expect at the JupyterCon 2018 Business Summit
One of our goals is to bring Jupyter’s enterprise use cases and practices into one place.
Winner of the Top Innovator Award at AI NY 2018: temi
The personal robot temi refactors robotic human behaviors we encounter in the “iPhone Slump,” and moves those back to actual robots.
The Intertwingularity is near: When humans transcend print media
Both reproducible science and open source are necessary for collaboration at scale—the nexus for that intermingling is Jupyter.
Jupyter is where humans and data science intersect
Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.
Jupyter Pop-up coming to Boston on March 21
Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.
Why self assessments improve learning
O’Reilly’s assessment tool puts the focus on the learner, not arbitrary scores.
How do you learn?
Shared learning: It's what we do at O'Reilly, and it's what we’d like to share with you.
Upcoming live training: Data
O'Reilly's live training events offer instructor-led, hands-on courses, with an emphasis on the social aspects of learning.
Probabilistic data structures in Python
Use approximations with error bounds to trade-off system resources, e.g., memory or compute time -- especially for large-scale analytics and streaming data.
Learn alongside innovators, thought-by-thought, in context
Introducing Oriole Online Tutorials, a new medium that lets you see, hear, and experiment at the same time and in the same place.
Probabilistic data structures in Python
Use approximations with error bounds to trade-off system resources, e.g., memory or compute time -- especially for large-scale analytics and streaming data.
Building data science teams: Preparing your organization
How should you prepare when assembling and integrating a data science team into your organization? In this video training segment, Paco Nathan offers tips to consider in the early stages, including designating the right executive sponsor and encouraging basic hands-on data science training for management.
Computational Thinking: Just Enough Math
The webcast introduces advanced math for business people, including graph theory, abstract algebra, optimization, bayesian statistics, and more advanced areas of linear algebra.
Learning Apache Mesos
Resources for getting started with Apache Mesos.
Apache Mesos: Open source datacenter computing
Mesos offers reliability, efficiency, and faster developer productivity.