A/B testing at scale provides opportunities for learning analytics researchers to learn from large sample sizes. Deploying and running live intervention experiments with such large samples, however, raises infrastructural challenges. This paper discusses some of those challenges, and reports on two possible implementations that address those challenges in a workforce learning context at a large technology
