Why a data scientist is not a data engineer
Or, why science and engineering are still different disciplines.
Data science ideas and resources.
Ideas
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
Automating ethics
Machines will need to make ethical decisions, and we will be responsible for those decisions.
Proposals for model vulnerability and security
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
Lessons learned building natural language processing systems in health care
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context.
Core technologies and tools for AI, big data, and cloud computing
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
Learning
Video
A lesson in prescriptive modeling
Simulate new business models and practices with open source code.
Runnable code
Probabilistic programming from scratch
Working with uncertainty in real-world data.
Video
The Offline Challenge: Delivering Mobile Apps that Always Work
Wayne Carter and Ali LeClerc show you how to build a mobile app that has a consistent user experience, both online and offline.
Runnable code
Strategies to validate your security detections
Machine learning to generate attack data and improve security analytics.
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.