How we created an illustrated guide to help you find your way through the data landscape.
Flash flood prediction using machine learning has proven capable in the U.S. and Europe; we're now bringing it to East Africa.
An interview with Greg Meddles, technical lead for healthcare.gov.
The better prepared you are to utilize all the data in your data lake, the more likely you are to be successful.
Validating your data requires asking the right questions and using the right data.
A peek into the clickstream analysis and production pipeline for processing tens of millions of daily clicks, for thousands of articles.
What data scientists need to know about production—and what production should expect from their data scientists.
Best practices and scalable workflows for reproducible data science.
Putting deep learning into practice with new tools, frameworks, and future developments.
Drew Paroski and Gary Orenstein on the rapid spread of machine learning and predictive analytics
How bots, threat intelligence, adversarial machine learning, and deep learning are impacting the security landscape.
Evaluating the state and development of Scala from a data engineering perspective.
The telecommunication industry’s unique position for new revenue opportunities in big data, IoT, and VR
Telcos must regain value from over-the-top services and develop new sources of revenue by leveraging their data and infrastructure.
The O’Reilly Podcast: John Thuma on how businesses can get more than “what happened” from their data.
The O’Reilly Podcast: Bob Montemurro on planning data systems to match needs.
Technical and policy considerations in combatting algorithmic bias.
Learning to act based on long-term payoffs.
Rather than hiring data scientists from outside, consider training your proto data scientists.
It's important in this age of big data to return the original meaning of serendipity and talk about it as a skill.
Deeper neural nets often yield harder optimization problems.
O'Reilly Podcast: Working with databases that go beyond traditional models.
A look at the data pipeline architecture for five key NERSC projects.
Close the time gap between analysis and action to bring about the next wave of improvements in efficiency and reliability—and magic.
This report explores how political data science helps to drive everything from overall strategy and messaging to individual voter contacts and advertising.
Why cross-channel analytics are crucial to empowering business teams with a behavioral view of your customer.
Start planning now to reap the many benefits of connected manufacturing.
The anatomy of an architecture to bring data science into production.
Analytic Ops—DevOps for data science—makes data analysis into a continually evolving process to meet business needs.
Rohit Jain takes an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all.
Systems with weak consistency guarantees can be expensive in unexpected ways.
How combining data and applying time-series techniques can provide insights into a company’s operational strengths and weaknesses.
Techniques to address overfitting, hyperparameter tuning, and model interpretability.
Sean Patrick Murphy describes how data science is helping electric utilities make sense of a stochastic world filled with increasing uncertainty, and reviews several cutting-edge tools for storing and processing big data.
Specialized technical tools are great, but sometimes a general contractor is the best approach.
Predictive-maintenance modeling requires a lot of work, but some can be automated.
With a focus on engineering and infrastructure, this O’Reilly report examines the tools and best practices that leading financial firms are using to migrate data to the cloud, build customer event hubs, and adhere to new rules for governance and security.
This report dives into the IoT industry through a series of illuminating talks and case studies presented at 2015 Strata + Hadoop World Conferences in San Jose, New York, and Singapore.
The difference between failure and success may be the difference between making analytics possible and making it straightforward.
How decoupling, optimization, and specialization resemble connective systems in our bodies.
How well prepared is your organization to innovate, using data science? In this report, two leading data scientists at Booz Allen Hamilton describe 10 characteristics of a mature data science capability.
Daniele Quercia discusses mapping city scents, computational social science, and using sharing economy data to help shape city regulations.
Measure your model’s business impact, not just its accuracy.
How in-page analytics and design thinking produces a rich, functional product.
A new O’Reilly report explores global trends in data analytics for the Industrial IoT.
The results our algorithms produce must be approachable for an ever-growing audience.
The O’Reilly Podcast: Eliot Knudsen on the business value of prescriptive analytics and machine learning algorithms.
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
Lessons from building a large-scale machine learning pipeline at Indeed.
Exploring how to “right-size” your infrastructure with Amazon Web Services.
Your data visualization and analytics front end is competing with the best of the Web—make it good.
Streaming analytics has been tested against the toughest judge—people—and now it’s ready to boss around robots.
Insights on process and culture from The Climate Corporation’s Erik Andrejko.
Finding patterns isn't really a question about random processes; it's a question about the human brain.
Apache Spark eyed as potential framework for big data analysis at one of the world’s most prominent nuclear research organizations.
Real-time automation is the key to Hadoop performance at scale.
Making a case for Big-Data-as-a-Service.
Machines can respond to data at machine-speed with streaming analytics and stateful services.
A look at early excitement and experimentation offers a glimpse into the future.
To document enterprise data, machines must learn from the explicit feedback and implicit signals people leave behind.
From intelligent investigation to cloud “security-as-a-service,” what you need to know for 2016.