Machine learning hyperparameter optimization with Argo

Canva uses a variety of machine learning (ML) models, such as recommender systems, information retrieval, attribution models, and natural language processing for various applications. A typical problem is the amount of time and engineering effort in choosing a set of optimal hyperparameters and configurations used to optimize a learning algorithm's performance. Hyperparameters are parameters set before a model's learning procedure begins. Hyperparameters, such as the learning rate and batch sizes, control the learning process and affect the predictive performance. Some hyperparameters might also have a significant impact on model size, inference throughput, latency, or other considerations. The number of hyperparameters in a model and their characteristics form a search space of possible combinations to optimize.