The O’Reilly Data Show Podcast: Michael Freedman on TimescaleDB and scaling SQL for time-series.
An algorithm for rubber-banding random points.
Jupyter for sharing and prototyping, Jupyter in academia, and FAIR principles.
Exploring a reference architecture solution.
To succeed in digital transformation, businesses need to adopt tools that enable collaboration, sharing, and rapid deployment. Jupyter fits that bill.
Karley Yoder on what GE Healthcare has learned as it embraces artificial intelligence.
The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.
How to understand machine learning adoption in the enterprise.
Giving context to code, human-in-the-loop design pattern, and collaborative documents.
Ring stacking games. With computers.
The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.
Overcome three types of debt to ship quality machine learning code.
A new role focused on creating data products and making data science work in production.
Approaches to data analysis, iterative workflows, and writing a book with Jupyter.
The O'Reilly Podcast: Ken Krupa on the challenge of data integration, and a solution.
Getting started with data science, Jupyter as shareable hub, and JupyterLab adoption.
Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.
Jupyter as a learning tool, the JupyterHub Project, and Music21.
Script generation from RNNs, Tensorflow book companion notebooks, transportation insights from notebooks, machine learning notebooks.
Tom Smith explains how the UK's Office of National Statistics is using data science to create repeatable, accurate, and transferable statistical research.
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.
Grace Huang shares lessons learned from running and interpreting machine-learning experiments.
Ziya Ma outlines the challenges for applying machine learning and deep learning at scale and shares solutions that Intel has enabled for customers and partners.
The O’Reilly Data Show Podcast: Jeremy Stanley on hiring and leading machine learning engineers to build world-class data products.
Project Jupyter co-founder Brian Granger on the JupyterLab project, its potential role in scientific and tech communities, and the expanding role of notebooks.
Aurélie Pols draws a broad philosophical picture of the data ecosystem and then hones in on the right to data portability.
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.
Darren Strange asks: What part will we each play in what is sure to be one of the most exciting times in computer science?
Using the music industry as an example, Paul Brook shows how modern information points bring new data that changes the way an organization will make decisions.
M. C. Srivas covers Uber's big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.
Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used.
TensorFlow cookbook materials, source notebooks, Python lectures, and Software Carpentry.
Teresa Tung on building a business case for the Internet of Things.
Bas Geerdink details the technology stack for real-time account forecasting at ING, and outlines how Spark is used for outbound communications.
Access to critical data in real time enables workers to generate insights from large amounts of information.
TSFRESH, 100 days of algorithms, how JupyterHub tamed big science, colorizing photos.
The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.
Executive reading: Why you need to democratize data.
A data-driven study of the complete Internet of Things (IoT) market.
Opinionated Docker stacks, Jupyter Themes, Jupyter in the bank, and Zuckerberg's man in the lab.
The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.
Metadata is central to a modern data architecture.
How to hire the right team and reorganize into a data-driven organization.
A possible solution to the complexities that plague big data projects.
JupyterDay Philly, Harmonics deep dive, Jupyter building blocks, and autoencoded Pokémon.
A look at Apache Kylin’s architecture and features in version 2.0.
Python cheat sheet, open source DL guide, Keen IO, and digital signal processing.
This excerpt from Jake VanderPlas' Python Data Science Handbook
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Three models for how automakers could partner with fleet operating companies to provide autonomous vehicles for on-demand mobility.
Reproducibility, TensorFlow examples, the new NBA, and 30,699 Kobe Bryant shots.
June Andrews talks about simple, cost-effective algorithmic computing at scale.
Kurt Brown discusses services in use, such as Genie, Metacat, Charlotte, and Microbots.
There’s money to be made in exhaust data (not just data exhaust).
Merging the gaps between data science and engineering, and what each side can learn from the other.
Tools, trends, what pays (and what doesn’t) for data professionals in Europe
The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.
Scientific use cases show promise, but challenges remain for complex data analytics.
Andra Keay discusses the five laws of robotics design.