The Modulai team joined forces with the engineers at Fishbrain to develop and deploy a machine learning based service that predicts what fish an angler is likely to catch a given day anywhere in the world.
Data from 2.5 millions catches was collected and refined, and each data point was annotated with a date, time, location and species of fish. Detailed weather information including air temperature, air pressure, wind speed, cloud cover and precipitation were added to each catch. Temporal data was transformed into astronomical conditions such as moon phase, solar irradiation and azimuth angle. A global climate model was created, providing information on historical weather patterns, vegetation and geology. Given its input, the final model predicts the likelihood of catching each of a large set of tracked fish species over the course of the day and powers BiteTime™, Fishbrain’s fishing forecast utility.
With more than 7 million users Fishbrain is the world’s largest fishing app, connecting anglers and allowing technology to change the way people fish. Users can log catches, share photos, make comments and also find the right gear for maximizing the chance of success on the next fishing trip.
How Modulai did it
Catch data was collected and combined with various internal and external sources. Data pipelines were developed for processing data from internal and external API’s. Various cloud managed services (such as Amazon Sagemaker and Lambda functions) were utilized for efficient model training, tuning and serverless deployment. Development as well as deployment was mainly performed and orchestrated in Python and open source libraries.