Data science without borders
Wes McKinney makes the case for a shared infrastructure for data science.
Data
Our take on the ideas, information, and tools that make data work.
Labz ‘N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud
Demba Ba explains how he designed and implemented two Harvard courses that use cloud-based Jupyter notebooks.
The current state of applied data science
Recent trends in practical use and a discussion of key bottlenecks in supervised machine learning.
How Ray makes continuous learning accessible and easy to scale
The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.
How to choose a cloud provider
Practical questions to help you make a decision.
Why AI and machine learning researchers are beginning to embrace PyTorch
The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and on deep learning within Facebook.
How big data and AI will reshape the automotive industry
The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.
The SMACK stack
A new architecture for today’s data-rich modern applications.
A framework for building and evaluating data products
The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.
Building a next-generation platform for deep learning
The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.
A scalable time-series database that supports SQL
The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.
What is Jupyter?
To succeed in digital transformation, businesses need to adopt tools that enable collaboration, sharing, and rapid deployment. Jupyter fits that bill.
Programming collective intelligence for financial trading
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
When will automation reach your industry?
How to understand machine learning adoption in the enterprise.
Creating large training data sets quickly
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
What is machine learning debt?
Overcome three types of debt to ship quality machine learning code.
What are machine learning engineers?
A new role focused on creating data products and making data science work in production.
The machine learning paradox
Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.
Lessons from piloting the London Office of Data Analytics
Eddie Copeland explores how the London Office of Data Analytics overcame the barriers to joining, analyzing, and acting upon public sector data at city scale.
Enabling data science in the enterprise
Tom Smith explains how the UK's Office of National Statistics is using data science to create repeatable, accurate, and transferable statistical research.
Peeking into the black box: Lessons from the front lines of machine-learning product launches
Grace Huang shares lessons learned from running and interpreting machine-learning experiments.
Data science and deep learning in retail
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
The science of visual interactions
Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.
What Kaggle has learned from almost a million data scientists
Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.