Machine learning for procurement analytics
The O’Reilly Podcast: Eliot Knudsen on the business value of prescriptive analytics and machine learning algorithms.
Data science
Data science ideas and resources.
From Insight-as-a-Service to insightful applications
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
Algorithms and architecture for job recommendations
Lessons from building a large-scale machine learning pipeline at Indeed.
Time-series analysis on cloud infrastructure metrics
Exploring how to “right-size” your infrastructure with Amazon Web Services.
Are your apps doing enough for your end users?
Your data visualization and analytics front end is competing with the best of the Web—make it good.
Fast data is for people
Streaming analytics has been tested against the toughest judge—people—and now it’s ready to boss around robots.
Building a high-throughput data science machine
Insights on process and culture from The Climate Corporation’s Erik Andrejko.
CERN seeks to predict new and popular data sets
Apache Spark eyed as potential framework for big data analysis at one of the world’s most prominent nuclear research organizations.
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.
The Hadoop tipping point
Real-time automation is the key to Hadoop performance at scale.
Hadoop in the cloud
Making a case for Big-Data-as-a-Service.
The IoT is a natural ecosystem for streaming analytics
Machines can respond to data at machine-speed with streaming analytics and stateful services.
What’s next for big data applications?
A look at early excitement and experimentation offers a glimpse into the future.
How human-machine collaboration has automated the data catalog
To document enterprise data, machines must learn from the explicit feedback and implicit signals people leave behind.
4 trends in security data science
From intelligent investigation to cloud “security-as-a-service,” what you need to know for 2016.
Predictive modeling: Striking a balance between accuracy and interpretability
Tips for using machine learning models in regulated industries.
Delivering data “as you like it” with self-service
Collaboration and preparation are key.
Is my developer team ready for big data?
A business leader’s guide to beginning the big data journey.
Drowning in data, starved for insight
Worth a billion dollars? The proof of the press release is in the data exhaust.
Distributed systems performance solutions require real-time intelligence
The O’Reilly Podcast: Pepperdata CEO Sean Suchter discusses why today's best practices fall short.
Approaching big data from a business perspective
The O’Reilly Podcast: Zaloni CEO Ben Sharma on the business value of real-time data, the data lake, and sentiment analysis.
Transactional streaming: If you can compute it, you can probably stream it
In the race to pair streaming systems with stateful systems, the real winners will be stateful systems that process streams natively and holistically.
Streaming data + stream processing = information
John Hugg discusses the business payoffs of stream processing with transactions.
Educating data
Using data science to improve learning, motivation, and persistence