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Using a single cloud provider is a thing of the past.
Practical questions to help you make a decision.
Tamr’s Eliot Knudsen on algorithms that work alongside human experts.
Jupyter in education, Jupyter-in-the-loop, and reproducibility in science.
A step-by-step tutorial on how to install and run JupyterHub on gcloud.
The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and on deep learning within Facebook.
A multi-model approach to transforming data from a liability to an asset.
A framework for moving from data to wisdom.
Learn how to use PixieDust in Jupyter Notebooks to create quick, easy, and powerful visualizations for exploring your data.
Authors Julia Silge and David Robinson discuss the power of tidy data principles, sentiment lexicons, and what they're up to at Stack Overflow.
Recapping winners of the Strata San Jose Startup Showcase.
The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.
Dimitar Filev on bringing cutting-edge computational intelligence to cars and the factories that build them.
It’s pretty easy to grasp the concept, but it’s a tricky algorithm to implement.
Stewart Rogers on building and managing products with embedded analytics.
A new architecture for today’s data-rich modern applications.
The O’Reilly Data Show Podcast: Pinterest data scientist Grace Huang on lessons learned in the course of machine learning product launches.
The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.
An algorithm that generates Bézier curves using an increasing number of control points.
Integrate and access any form of data using a multi-model database.
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.
Grace Huang shares lessons learned from running and interpreting machine-learning experiments.
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.
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.
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.
Darren Strange asks: What part will we each play in what is sure to be one of the most exciting times in computer science?
Watch highlights covering data-driven business, data engineering, machine learning, and more. From Strata Data Conference in London 2017.
M. C. Srivas covers Uber's big data architecture and explores the real-time problems Uber needs to solve to make ride sharing smooth.
Miriam Redi investigates how machine learning can detect subjective properties of images and videos, such as beauty, creativity, and sentiment.
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.