Under the hood of machine learning
Exploring a reference architecture solution.
Data science ideas and resources.
Ideas
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.
Integrating across silos with operational data hubs
The O'Reilly Podcast: Ken Krupa on the challenge of data integration, and a solution.
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.
Architecting actionable insights
Bas Geerdink details the technology stack for real-time account forecasting at ING, and outlines how Spark is used for outbound communications.
Learning
Runnable code
Repeatable sampling of data sets in BigQuery for machine learning
An efficient, fast, and repeatable selection method that works on very large data sets.
Video
Solving the right problem
Max Shron and Sasha Laundy explore tactics for need-finding and problem scoping that make it possible to put investments in data to profitable use.
Runnable code
Advancing Procurement Analytics
Learn how your company can significantly improve procurement analytics to solve business questions quickly and effectively.