One of the main problems in AutoML implementation is finding the best strategy to search the most optimal pipeline in prediction or classification tasks. This problem is commonly known as CASH (Combined Algorithm Selection and Hyperparameter Optimization). This talk will show competitive results with significantly shorter computation time by just focusing the search in the model selection and structure of the pipeline without the need of hyperparameter optimization.

Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. Sustaining this rapid expansion requires data-driven management of the production process and environmental impacts. This talk presents a machine-learning-based exploration of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada using AutoML tools in Julia.