Or, why science and engineering are still different disciplines.
Why companies are turning to specialized machine learning tools like MLflow.
Machines will need to make ethical decisions, and we will be responsible for those decisions.
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
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.
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
Simulate new business models and practices with open source code.
Working with uncertainty in real-world data.
Wayne Carter and Ali LeClerc show you how to build a mobile app that has a consistent user experience, both online and offline.
Machine learning to generate attack data and improve security analytics.
An efficient, fast, and repeatable selection method that works on very large data sets.
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.