Teams building AI features often hear two instructions that sound alike: test the system, and red team the system. They are related, but they answer different questions. AI testing checks whether a system does what it is supposed to do. AI red teaming asks how someone could make it do what it should not. Treating them as the same thing leaves gaps, and doing only one means finding problems in production instead of before launch. This blog explains what each practice is, how t

Automated machine learning promises to shorten the path from data to a working prediction model, but the platform a team picks shapes far more than the first model. It decides how easily models can be explained to auditors, how they reach production, how much a data science team can customize, and how closely the work is tied to a particular cloud. DataRobot, H2O.ai, Azure Machine Learning, and Google Vertex AI are four of the most common choices, and each reflects a differen